Yapay Zekâ
ABD merkezli yapay zeka şirketi Natural, A Serisi yatırım turunda 30 milyon dolar fon topladı. Kirsten Green'in Forerunner Ventures'ının liderlik ettiği bu yatırım, Natural'ın yapay zeka ajanları geliştirme ve bu alandaki inovasyonunu hızlandırma hedeflerini destekleyecek. Şirket, bu fonu ürün geliştirme ve pazar genişlemesi için kullanmayı planlıyor.
Yapay Zekâ
Jack Dorsey, iş süreçlerini optimize etmeyi hedefleyen yeni iletişim ve proje yönetim platformu Buzz'ı duyurdu. Yapay zeka ajanlarını insanlarla bir araya getirerek ekip çalışmasını dönüştürmeyi amaçlayan Buzz, özellikle Slack gibi mevcut araçlara alternatif arayan şirketler için dikkat çekici bir çözüm sunuyor.
Yapay Zekâ
Amazon SES, e-posta gönderme, alma ve izleme özelliklerine erişimi ve satın almayı kolaylaştırmak amacıyla Essentials, Pro ve Enterprise adında yeni fiyatlandırma planlarını kullanıma sundu. Bu hamle, bireysel yetenek değerlendirmesi ihtiyacını ortadan kaldırarak farklı kurumsal ihtiyaçlara özel, paketlenmiş çözümler sunmayı hedefliyor.
Yapay Zekâ
Yüksek ağ verimiyle bilinen ve 3. Nesil Intel Xeon Ölçeklenebilir işlemcilerle desteklenen Amazon EC2 M6in ve M6idn örnekleri, artık AWS Asya Pasifik (Haydarabad) ve Güney Amerika (Sao Paulo) bölgelerinde de erişilebilir durumda. Bu genişleme, bu önemli pazarlardaki geliştiricilere ve işletmelere veri analizi, oyun ve yapay zeka/makine öğrenimi uygulamaları gibi zorlu iş yükleri için daha fazla seçenek sunuyor.
Yapay Zekâ
AWS, Amazon EC2 R6in ve R6idn örneklerinin Avrupa (Paris) ve Kanada (Merkez) bölgelerine genişletildiğini duyurdu. Altıncı nesil, Intel Xeon destekli bu örnekler, 200 Gbps'ye kadar ağ bant genişliği ve 80 Gbps EBS bant genişliği sağlayarak ağ sanal cihazları, güvenlik duvarları ve yük dengeleyiciler gibi ağ yoğun uygulamalar için idealdir. Bu genişleme, bu bölgelerdeki müşterilere kritik altyapı için yüksek performanslı, maliyet etkin bilgi işlem erişimi sağlıyor.
Yapay Zekâ
Google, yapay zeka modeli ailesini Gemini 3.6 Flash, Gemini 3.5 Flash-Lite ve siber güvenliğe odaklanan Gemini 3.5 Flash Cyber ile güçlendirdi. Bu yeni modeller, özellikle yüksek hacimli üretim ve kritik güvenlik ihtiyaçları için tasarlanmış olup, daha hızlı ve verimli yapay zeka çözümleri sunmayı hedefliyor. Şirket, bu adımla yapay zekanın çeşitli sektörlerdeki kullanımını derinleştirmeyi amaçlıyor.
Yapay Zekâ
Jake Mannix discusses moving AI agents past chaotic "1970s BASIC" architectures. He shares how implementing an intermediate protocol layer allows engineering leaders to build versioned, encapsulated "virtual tools." This design enables interface mapping, dynamic schema projection, and runtime taint tracking to proactively eliminate data exfiltration risks without slowing velocity. By Jake Mannix (Kaynak dil: İngilizce.) Maestro News olayı Türkçe’ye çevirip Maestro Dev ekseninde özgün sektör analizi olarak yeniden çerçeveledi.
Yapay Zekâ
Anthropic detailed the containment architectures it uses for Claude across its products. It argues that agent safety depends on placing deterministic limits on an agent’s filesystem, network, and execution environment rather than on permission prompts or safeguards. Most notably, it examines failures at trust boundaries and along permitted egress paths that led Anthropic to revise those designs. By Eran Stiller (Kaynak dil: İngilizce.) Maestro News olayı Türkçe’ye çevirip Maestro Dev ekseninde özgün sektör analizi olarak yeniden çerçeveledi.
Yapay Zekâ
Google Cloud has published a new blueprint setting out how organisations should secure artificial intelligence workloads running on Google Kubernetes Engine, arguing that the shift from prototype to production has outpaced traditional security models. The document sets out a three layer approach covering infrastructure, model integrity and application security. By Matt Saunders (Kaynak dil: İngilizce.) Maestro News olayı Türkçe’ye çevirip Maestro Dev ekseninde özgün sektör analizi olarak yeniden çerçeveledi.
Yapay Zekâ
Introducing OpenAI Presence, a proven enterprise AI agent platform that helps organizations deploy trusted voice and chat agents for customer and internal workflows. Maestro News, gelişmeyi Maestro Dev ekseninde — yazılım, yapay zekâ, bulut ve ürün teslimatı — özgün Türkçe dilde yeniden çerçeveliyor.
Yapay Zekâ
Amazon Managed Service for Prometheus now supports up to 1.5 billion active metric time series and up to 200,000 total recording and alerting rules per workspace. Customers can also create many workspaces per account, enabling the storage and analysis of billions of Prometheus metrics across their organization. Amazon Managed Service for Prometheus is a fully managed, Prometheus-compatible monitoring service that makes it easy to monitor and alert on operational metrics at scale. It automatically scales ingestion and storage for high-cardinality workloads across containerized, serverless, and hybrid environments, and integrates with AWS security services for fast, secure access to data. To get started, create an Amazon Managed Service for Prometheus workspace and increase your workspace active series or rules limits by filing a service limit increase request in AWS Support Center or AWS Service Quotas. Maestro News, gelişmeyi Maestro Dev ekseninde — yazılım, yapay zekâ, bulut ve ürün teslimatı — özgün Türkçe dilde yeniden çerçeveliyor.
Yapay Zekâ
Today, Amazon Elastic Container Service (Amazon ECS) introduces Action Logs, a new observability feature that delivers detailed, timestamped records of the actions Amazon ECS performs on behalf of customers during service deployments and ECS Managed Daemon updates. By surfacing service-side operations that were previously invisible, Action Logs help you monitor and troubleshoot your workloads directly, without contacting AWS Support or manually correlating data from multiple sources. With Action Logs, you gain visibility into key deployment state transitions of service deployments, Managed Daemon updates. Each log entry includes the event name, log level(INFO, WARN, OR ERROR), relevant resource ARNs, and a status reason, helping you reduce mean time to resolution when issues arise. You can opt in at the cluster level through the Amazon ECS console or by using Amazon CloudWatch vended logs APIs, and choose to deliver logs to Amazon CloudWatch Logs, Amazon S3, or Amazon Kinesis Data Firehose depending on your operational needs. At launch, Amazon Q in the Amazon ECS console integrates with Action Logs to automatically detect deployment issues such as circuit breaker rollbacks and unstable service revisions, providing customers with root cause analysis, resource-level comparisons, and step-by-step remediation guidance without leaving the console. Standard CloudWatch Logs, Amazon S3, or Amazon Data Firehose pricing applies for log ingestion and storage. For pricing details, see Amazon CloudWatch Pricing. Amazon ECS Action Logs are available in all AWS Regions, including the AWS GovCloud (US) Regions. To learn more, refer Monitor Amazon ECS operations with Action Logs in Amazon ECS Developer Guide. Maestro News, gelişmeyi Maestro Dev ekseninde — yazılım, yapay zekâ, bulut ve ürün teslimatı — özgün Türkçe dilde yeniden çerçeveliyor.
Yapay Zekâ
AWS, Elastic Disaster Recovery (DRS) hizmetinin Asya Pasifik (Bangkok, Malezya, Yeni Zelanda, Taipei), Kanada Batı (Calgary) ve Meksika (Merkez) dahil olmak üzere altı yeni bölgeye genişletildiğini duyurdu. Bu stratejik hamle, bu büyüyen pazarlardaki müşteriler için veri dayanıklılığı ve iş sürekliliği seçeneklerini önemli ölçüde artırarak kesintilerden daha hızlı kurtulmayı ve arıza sürelerini en aza indirmeyi sağlıyor.
Yapay Zekâ
Google Cloud, Cloud Run hizmetleri için çok bölgeli yüksek erişilebilirliği kolaylaştıran yeni özellikler sunarak, geliştiricilerin karmaşık altyapı kurulumları olmadan bölgesel dayanıklılık ve otomatik hata kurtarma sağlamasına olanak tanıyor. Bu güncellemeler, küresel yük dengeleme ve akıllı trafik yönetimi entegrasyonu sayesinde kesinti riskini azaltırken, iş sürekliliğini ve müşteri memnuniyetini artırıyor.
Yapay Zekâ
Bu makale, Google Cloud'daki Kimlik ve Erişim Yönetimi (IAM) sistemini güçlendirmenin temel prensiplerini ve pratik adımlarını inceliyor. En az ayrıcalık ilkesini, koşullu erişim politikalarını ve bağlama duyarlı güvenlik kontrollerini vurgulayarak, kuruluşların bulut ortamlarında yetkisiz erişimi nasıl önleyebileceğini ve veri güvenliğini nasıl sağlayabileceğini açıklıyor. Amaç, esnekliği korurken güvenlik duruşunu önemli ölçüde iyileştirmektir.
Yapay Zekâ
Checkout.com, veri orkestrasyonunu rakip bir bulut platformundaki kendi yönettiği Apache Airflow ortamından Google Cloud Composer 3'e başarıyla taşıdı. Bu stratejik hamle, altyapı bakımının operasyonel yükünü büyük ölçüde azaltmayı, veri mühendisliği ekiplerinin yenilikçi veri ürünleri oluşturmaya ve iş içgörülerini hızlandırmaya odaklanmasını sağlamayı hedefliyor. Geçiş, karmaşık veri iş akışları için yönetilen hizmetlerin faydalarını vurguluyor.
Yapay Zekâ
Across 101 enterprises, the infrastructure that feeds AI agents their business context is being built faster than it can be trusted. Retrieval-augmented generation is already the default context source, and provider-native retrieval has quietly overtaken the dedicated vector databases that define the category — yet a majority of enterprises have already watched their agents produce confident, wrong answers traced to missing or inconsistent context. A governed semantic layer is emerging as the fix, but most are still building it; the field is converging on hybrid retrieval; and even as provider-native tools lead in practice, a plurality say they intend to keep best-of-breed. The result is a context gap — agents that sound authoritative running on a foundation their owners do not yet fully trust. This wave of VentureBeat Pulse Research examines the enterprise RAG and context layer: what feeds AI agents their business context, which retrieval systems enterprises run, how they buy and measure them, where the architecture is heading, and — most revealingly — how often that context is already failing them. The central finding is a context gap — the distance between how confidently enterprise agents answer and how reliable the context beneath them actually is. A majority of enterprises (57%) report that in the past six months their AI agents produced confident but wrong answers they traced to missing or inconsistent business context, and more than half of those said it happened more than once. This is not a fringe failure: retrieval is the primary context source for 38% of enterprises, more than any other approach, so when retrieval is thin or inconsistent, the errors it produces are wearing the agent’s authority. The infrastructure to fix it is being built — 58% already run or are building a governed semantic layer — but for most it is not yet in production. Underneath, the market is consolidating in a direction that surprises. Provider-native retrieval — OpenAI’s file search (40%) and Google’s Vertex AI Search (38%) — already leads every dedicated vector database, and enterprises expect hybrid retrieval to dominate by the end of 2026 (34%). Yet a plurality (36%) say they intend to keep best-of-breed standalone tools rather than consolidate onto a provider’s native context stack, and a majority (57%) plan to switch or add a provider within the year. Stated preference and actual usage are pulling in opposite directions — the market is buying provider-native while insisting it wants independence. Methodology VentureBeat fielded this survey as part of its ongoing Pulse Research series. This survey focused on enterprise RAG infrastructure and the context layer — the retrieval systems, semantic layers, and context sources that feed AI agents. Responses are filtered to organizations with more than 100 employees (n=101); the survey drew no responses from organizations of 100 or fewer, so the full sample qualifies. All responses are from a single Q2 2026 (June) wave, so the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%. By organization size the sample concentrates in the mid-market: 251–1,000 employees (31%) and 101–250 (31%) lead, with 1,001–5,000 (20%), 5,001–10,000 (12%), and 10,001+ (7%) above them. By role it spans managers (39%), individual contributors (27%), the C-suite (16%), and VPs and directors (14%); on purchasing authority it is buyer-credible, with 46% final decision-makers and another 26% recommenders or influencers. Technology/Software is the largest industry at 20%, followed by Healthcare/Life Sciences (11%) and a broad spread across retail, transportation, financial services, manufacturing, and education. At 101 respondents this is a modest sample and should be read as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It is best read as the view from organizations actively standing up RAG and context infrastructure rather than from the largest operators. Finding 1: Confident and wrong More than half have traced agent errors to bad context We asked whether, in the past six months, enterprises had traced a confident but wrong agent answer to missing or inconsistent business context. Most had. This is the report’s defining number. A majority of enterprises (57%) have already had an AI agent produce a confident, wrong answer they traced to bad context — wrong metrics, stale definitions, or missing documents — and more than half of those have seen it happen more than once. Only 28% report no such failure, and a small remainder either don’t run agents on enterprise data or don’t trace root cause closely enough to know. The failure mode is specific and dangerous: the model is not obviously hallucinating; it is confidently wrong because the context feeding it was thin or inconsistent. Everything else in this report — what enterprises retrieve, how they govern it, and what they plan to build — is downstream of this problem. Finding 2: RAG is the default context source Retrieval feeds more agents than any other method We asked what an enterprise’s AI agents primarily use to understand its data. Retrieval leads by a wide margin. Retrieval is the backbone of enterprise context. For 38% of organizations, RAG over documents or a vector index is the primary way agents understand the business — nearly twice the share of the next approach, a governed semantic layer or ontology (21%). Mixed approaches (14%), direct live-system queries (10%), and long-context loading (6%) fill out the rest, and only 2% let agents run on the model’s general knowledge alone. The concentration matters in light of Finding 1: because so much enterprise context flows through retrieval, the quality of that retrieval is the quality of the answer. When RAG is the default source, thin retrieval is not an edge case — it is the main failure surface. One approach is notable for its absence from these answers: customizing model weights, also known as fine-tuning. Every leading source of business context is injected at run time. Our most recent direct measurement of fine-tuning comes from our April–May survey wave (a separate survey, n=136), where fine-tuning capabilities ranked last of six factors in model selection at 5% — even as 26% of that sample still named fine-tuning and customization an investment they expect to grow. Fine-tuning has fallen out of the primary selection conversation; context injection is how enterprises make agents knowledgeable about their business. Finding 3: Provider-native retrieval already leads the vector databases OpenAI file search and vertex AI search top the dedicated tools We asked which retrieval systems enterprises run in production today. The answer favors the model providers and hyperscalers over the specialists. The dedicated vector database is no longer the center of the RAG stack. OpenAI’s file search (40%) and Google’s Vertex AI Search (38%) lead — provider-native and hyperscaler-native retrieval — ahead of every purpose-built vector database. Among the specialists, the most-used is the one enterprises already run for other reasons (Elasticsearch/OpenSearch, 20%) and the open, embedded option (pgvector, 12%); the pure-play vector databases that define the category — Weaviate, Qdrant, Pinecone, Milvus — each sit in single digits to low double digits. Notably, 13% of enterprises say they still run no production RAG at all. As with the platforms in the parallel infrastructure wave, enterprises are gravitating to retrieval that comes bundled with tools they already buy. The shape of this finding held across both Q2 waves. In April–May (n=161), provider-built retrieval led usage there too, while every dedicated vector database remained marginal — the most-used standalone vector database peaked at 8% of that sample — and the hybrid, pluralistic future was already the consensus expectation (34% expected hybrid retrieval to dominate, with another 29% expecting multiple architectures by use case). Two waves, consistent picture: the category that coined the “vector database” term is being collected by the platforms enterprises already buy from. Finding 4: But they say they want to keep best-of-breed A plurality resist consolidating onto a provider’s native stack We asked how enterprises will respond as model providers bundle retrieval, memory, and orchestration into their platforms. Their stated intent cuts against their current usage. Here is the tension at the heart of the stack. Even as provider-native retrieval leads in practice (Finding 3), a plurality of enterprises (36%) say they intend to keep best-of-breed standalone tools rather than consolidate onto a provider’s native context stack — well ahead of the 21% who plan to consolidate. Another 21% expect a mix, and 9% intend to build and own the layer themselves. The gap between what enterprises run and what they say they want is the strategic question of the category: they are adopting bundled retrieval for convenience while asserting they will preserve independence. Which impulse wins — the pull of the provider bundle or the stated preference for modular control — will shape the retrieval market more than any single tool. Finding 5: Hybrid retrieval is the consensus bet Vector-only retrieval is already seen as insufficient We asked which retrieval architecture enterprises expect to dominate their production RAG systems by the end of 2026. The field is converging — with a large share still unsure. The architecture is settling on hybrid. A third (34%) expect hybrid retrieval — embeddings combined with reranking and access controls — to dominate their production systems by the end of 2026, three times the 11% who expect vector-only retrieval to prevail. That is a notable signal: the pure vector-search approach that launched the category is already viewed as insufficient on its own, superseded by pipelines that add reranking for accuracy and access controls for governance — the very access controls whose absence produces the failures in Finding 1. Tellingly, the second-largest answer is uncertainty: 17% simply don’t know, and another 14% expect to move beyond a dedicated vector layer entirely toward tool-first or long-context retrieval. The consensus is not a single tool but a layered pipeline — and it is not yet fully formed. Finding 6: The governed context layer is being built now Most run or are building a semantic layer — few in production We asked whether enterprises use a governed semantic or context layer to give agents and BI a shared understanding of their data. Most are on the path; fewer have arrived. The fix for the context gap is under construction. Well over half of enterprises (58%) either run a governed semantic layer in production (25%) or are piloting and building one (34%), and a further 17% are actively evaluating — meaning three-quarters are engaged with the idea in some form. But the balance is telling: more are building than have shipped, so for most enterprises the shared, governed definition layer that would prevent the "confident but wrong" failures of Finding 1 is still a work in progress. The semantic layer is the industry’s answer to inconsistent context; this wave catches it mid-construction, ambition well ahead of production. Finding 7: Bought on ingestion and simplicity, watched for correctness Selection favors operability; monitoring favors correctness and security We asked what matters most when enterprises choose a retrieval system, and what they track once it is running. Both answers lean practical. Enterprises choose retrieval systems on operability. Ease of data ingestion (36%), latency and performance (32%), and operational simplicity (29%) lead the selection criteria — ahead of retrieval accuracy and access control (23% each), the two factors most directly tied to the failures in Finding 1. Once systems are running, the emphasis shifts toward trust: the most-tracked metrics are response correctness (42%) and security and access control (38%), ahead of latency (28%), operational stability (27%), and answer relevance (23%). Satisfaction with current systems is moderately positive but not enthusiastic — on a five-point scale, overall satisfaction averages 4.0, with ease of implementation and value for money both near 3.9. Enterprises buy for how easily a system runs and watch it for whether it can be trusted. Finding 8: A retrieval reshuffle is coming A majority plan to change providers — and the vector specialists are gaining interest We asked whether enterprises plan to change or add a retrieval provider, and which they are considering. The consideration set differs from today’s stack. The retrieval stack is not settled. While 43% have no plans to change, a small majority (57%) intend to switch or add a provider within twelve months, and a quarter (26%) within the next quarter. The consideration set is where it gets interesting: provider-native retrieval still leads what enterprises are evaluating (OpenAI 22%, Vertex AI Search 21%), but the open-source vector specialists punch above their current footprint — Qdrant (14%) and Milvus (13%) draw more switching interest than their present usage (10% and 6%) would suggest. Read with Finding 4, the picture is a market in flux: enterprises run provider-native today, are evaluating a broader field, and say they want to keep their options open. The reshuffle ahead will test whether best-of-breed intent survives contact with the convenience of the bundle. The bottom line: A context gap that more retrieval alone won’t close Organizations with more than 100 employees are wiring agents into their business faster than they can guarantee the context those agents run on. Retrieval is the default source of enterprise context, and it increasingly comes from the model providers and hyperscalers rather than the dedicated vector databases — yet a majority of enterprises have already watched agents answer confidently and wrongly because that context was thin or inconsistent. The failure is not exotic; it is the predictable result of pointing authoritative-sounding agents at an unreliable foundation. The industry’s answer — a governed semantic layer, hybrid retrieval with reranking and access controls — is being built but is mostly not yet in production, and enterprises are pulled between the convenience of provider-native bundles and a stated preference for best-of-breed independence. At 101 respondents in a single Q2 wave this is a directional read, skewed toward the mid-market — but the direction is clear: the context layer is the next contested tier of the AI stack, and right now agents are running ahead of it. The context gap is not a retrieval-volume problem that more documents or bigger indexes will solve on their own; it is a problem of governed, consistent, access-aware context. The open question for later waves is whether enterprises finish building that layer before the confident-but-wrong failures move from the lab into decisions that matter. Based on survey responses from 101 qualified enterprise respondents (100+ employees), drawn from a single Q2 2026 (June) wave. At this sample size the results should be read as a directional signal rather than a precise measurement — it's a self-selected sample, not a probability sample, and skews toward the mid-market. Respondents include managers, individual contributors, VPs/directors, and the C-suite, with strong purchasing authority, across technology, healthcare, retail, transportation, financial services, manufacturing, and education. Maestro News, gelişmeyi Maestro Dev ekseninde — yazılım, yapay zekâ, bulut ve ürün teslimatı — özgün Türkçe dilde yeniden çerçeveliyor.
Yapay Zekâ
For a quarter century, the Google search box has been one of the most recognizable interfaces in computing: a thin white rectangle, a blinking cursor, a few typed words, and a list of blue links. On Tuesday, Google will formally retire that paradigm. At its annual I/O developer conference, Google announced a sweeping redesign of the search box itself — the literal text field where billions of queries begin every day — transforming it from a simple keyword input into a dynamic, AI-driven conversation starter that can accept text, images, PDFs, videos, and even open Chrome tabs as inputs. The company is also merging its AI Overviews and AI Mode features into a single, seamless search flow, eliminating the friction that previously forced users to choose between a traditional results page and an AI-forward experience. Liz Reid, Google's vice president and head of Search, called it "the biggest upgrade to our iconic search box since its debut over 25 years ago" during a press briefing on Monday. The announcement arrived alongside a blizzard of other news — new Gemini models, a personal AI agent called Spark, an intelligent shopping cart, a reimagined developer platform — but the search box redesign may prove to be the most consequential. It is the clearest signal yet that Google views the future of its flagship product not as a place where users type fragmented keywords, but as an interface where they hold open-ended, multimodal conversations with an AI system backed by the entire web. The new search box expands, accepts files, and coaches you on what to ask The changes show a fundamental shift in how Google expects people to interact with the product that generates the vast majority of Alphabet's revenue. The box itself now dynamically expands to accommodate longer, more conversational queries. Where the old interface subtly encouraged brevity — a narrow field suited to two- or three-word keyword strings — the new design invites users to fully articulate complex questions in granular detail. It also now supports multimodal inputs directly. Users can upload images, PDFs, files, and videos, or drag in content from Chrome tabs, right from the main search interface. Previously, some of these capabilities existed in AI Mode, but reaching them required extra steps. Now they sit at the primary entry point. Google is also deploying what it describes as an AI-powered query suggestion system that "goes beyond autocomplete." Rather than simply predicting the next word a user might type based on popular searches, the system helps users formulate complex, nuanced queries — essentially coaching them toward the kind of detailed questions that AI Mode handles best. The new search box is starting to roll out immediately in all countries and languages where AI Mode is available. Google is merging AI overviews and AI mode into one seamless experience Perhaps more significant than the box itself is the architectural change happening behind it. Google is unifying AI Overviews — the AI-generated summary panels that appear atop traditional search results — with AI Mode, the more immersive conversational search experience the company launched at I/O one year ago. Starting Tuesday, this merged experience will be live across mobile and desktop worldwide. A user can type a question, receive an AI Overview alongside traditional results, and then continue directly into a back-and-forth AI Mode conversation to ask follow-up questions — all without navigating to a separate interface. Reid explained the logic during the press briefing: the new AI search box is "an upgrade of our traditional search box, and so the results take you directly to main search rather than AI mode." She noted that while some power users actively sought out AI Mode, "for most users, they don't actually want to have to think about, do they want more of a traditional page or an AI-forward search experience." The goal, she said, was to ensure that "for most users, they don't have to think about where to go, they can just go to the search box they're familiar with, and it feels like they get the best experience afterwards." One billion users and doubling queries reveal how fast search behavior is shifting Google's decision to redesign the foundational interface of its most important product did not happen in a vacuum. The company shared a set of usage statistics during the briefing that reveal just how rapidly user behavior is already changing. AI Mode, which launched in the United States at I/O 2025, has surpassed one billion monthly users in its first year. AI Mode queries have been doubling every quarter since launch. AI Overviews, the lighter-weight AI summaries, now reach more than 2.5 billion monthly users. And overall search query volume hit an all-time high last quarter — a data point the company had previously disclosed on its earnings call. Sundar Pichai, Google's CEO, framed these figures as evidence that AI features are additive, not cannibalistic, to search usage. "When people use our AI-powered features in search, they use search more," he said. He added that he loves "how search has become less about individual queries and feels more like an ongoing conversation, giving users deeper insights and connecting you with the vastness of the web." Reid reinforced the point: "It's not just that people are searching more, it's that they're searching differently. They're fully expressing their questions in granular detail, asking those follow-up questions and searching across modalities." Gemini 3.5 Flash gives Google's AI search the speed it needs to work at scale Under the hood, the new search experience runs on Gemini 3.5 Flash, Google's newest AI model, which the company also introduced at I/O. Google upgraded AI Mode's underlying model to 3.5 Flash to deliver what Reid described as "an even more powerful AI search experience." Gemini 3.5 Flash is the workhorse of this year's announcements. Google claims it outperforms its previous frontier model, Gemini 3.1 Pro, on nearly all benchmarks while running four times faster in output tokens per second than comparable frontier models. Pichai described it as being "in a league of its own in the top right quadrant" of the Artificial Analysis index, which plots intelligence against speed — meaning it delivers near-frontier quality at dramatically lower latency. That speed matters enormously for search. A conversational AI search experience that feels sluggish would be dead on arrival for a product that serves billions of queries daily. By coupling the redesigned interface with a model optimized for both quality and throughput, Google is attempting to make AI-powered search feel as instantaneous as the old keyword experience — while being dramatically more capable. Search can now build interactive visuals and custom mini apps on the fly The redesigned search box is also the gateway to a set of new capabilities that push search far beyond text-based answers. Google announced what it calls "generative UI" — the ability for search to dynamically build custom widgets, interactive visualizations, and even mini applications in real time, tailored to a user's specific question. Reid offered a concrete example during the briefing: a user could ask "How do black holes affect space time?" and receive an interactive visual in an AI Overview that brings the concept to life. Follow-up questions would trigger the system to dynamically generate entirely new visuals in real time. This is possible, she explained, because of "a novel real-time code generation system we built in partnership with the Google DeepMind team" that runs on Gemini 3.5 Flash. Generative UI capabilities will roll out to everyone this summer, free of charge. But Google is going further still. For ongoing tasks — planning a wedding, organizing a move, tracking a fitness routine — users will be able to build what the company describes as customizable, stateful experiences within search, powered by its Antigravity development platform. These require no coding expertise. Users simply describe what they want in natural language, and search builds it. Those experiences will be available in coming months, starting with Google AI Pro and Ultra subscribers in the United States. AI agents that monitor the web around the clock are coming to search results The redesign also opens the door to what Google calls "information agents" — AI agents that users can configure directly within search to monitor the web 24/7 for specific conditions and deliver synthesized updates when those conditions are met. A user could, for example, set up an agent to track market movements in a particular sector with specific parameters. The agent would create a monitoring plan, tap into real-time finance data, and proactively notify the user when conditions are met — complete with links and context for further research. Other use cases include apartment hunting, tracking sneaker drops, or monitoring any topic a user cares about. Information agents will launch first for Google AI Pro and Ultra subscribers this summer. These agents sit within a much larger strategic pivot that Google articulated throughout the briefing: the company is going all-in on AI systems that don't just answer questions but proactively take actions on users' behalf. Beyond search, Google introduced Gemini Spark, a 24/7 personal AI agent that runs on dedicated virtual machines in Google Cloud. It unveiled the Universal Cart, an intelligent cross-merchant shopping cart. It announced the Agent Payments Protocol for agents to make secure purchases. And it expanded its Antigravity developer platform into a full ecosystem for building autonomous AI agents. Publishers, advertisers, and SEO professionals face a new reality The redesign raises profound questions for the sprawling ecosystem — publishers, advertisers, SEO professionals — that has been built around the old model of keyword search and blue links. If users increasingly express their needs as full, conversational sentences rather than fragmented keywords, the entire discipline of search engine optimization will need to evolve. Keyword-density strategies become less relevant when the AI is parsing natural language intent rather than matching strings. Content that answers deep, nuanced questions in authoritative ways becomes more valuable; content engineered to rank for two-word keyword fragments becomes less so. For publishers, the stakes are existential. AI Overviews already synthesize information from across the web and present it directly in search results, reducing the need for users to click through to source material. The new seamless AI Mode integration deepens that dynamic: users can now get an AI-generated answer and ask multiple follow-up questions without ever leaving the search page. Google has consistently maintained that its AI features drive more traffic to publishers, but the redesign puts that claim under renewed scrutiny as the search results page becomes more self-contained. For advertisers — who fund the vast majority of Google's revenue — the shift from keywords to conversations changes the calculus of ad targeting. Conversational queries contain richer intent signals, which could make ad targeting more precise and valuable. But they also create new ambiguities: when a user is in the middle of a multi-turn conversation with AI Mode, where does an ad naturally fit? Google did not detail changes to its advertising model during the briefing, but the structural shift in the interface will inevitably reshape how ads are surfaced and measured. The search box was always more than a product — it was a habit for billions of people There is a reason Google chose to redesign the search box rather than simply adding new features behind it. The search box is not just a product element at this point; it is a cultural artifact — one of the few pieces of digital infrastructure used by essentially the entire internet-connected world. Changing it sends an unmistakable message about where the company believes computing is headed. For 25 years, the search box trained billions of people to think in keywords — to compress their curiosity into the shortest possible string of words. The new box invites them to do the opposite: to think out loud, to upload what they're looking at, to ask follow-up questions, to let an AI system handle the compression. Pichai tied the company's broader ambitions to a striking statistic: Google's surfaces now process over 3.2 quadrillion tokens per month, up seven-fold from a year ago. The company expects capital expenditures of approximately $180 to $190 billion in 2026 — roughly six times the $31 billion it spent four years ago — largely to support the infrastructure required for this AI transformation. When asked about the future of traditional search, he was direct. "Search is the most used AI product in the world," he said. The blinking cursor in Google's search box still invites you to type. But after 25 years of teaching the world to speak in keywords, Google is now asking it to speak in sentences — and betting roughly $190 billion that it will. Maestro News, gelişmeyi Maestro Dev ekseninde — yazılım, yapay zekâ, bulut ve ürün teslimatı — özgün Türkçe dilde yeniden çerçeveliyor.
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Across 107 enterprises, AI infrastructure spending is accelerating well ahead of the ability to see or steer its economics. Most organizations run their AI on a familiar base of hyperscalers and model-provider APIs, yet the next dollar is aimed at specialized compute almost none of them use today; a majority intend to switch or add providers within the year, many within a quarter. Buying decisions turn on integration and total cost of ownership rather than headline token price — which is fortunate, because most enterprises cannot yet see their unit economics clearly: GPUs sit at half utilization or less, and fewer than half rigorously track what their compute actually costs. The result is a compute gap — heavy, fast-moving investment running ahead of the visibility needed to control it. This wave of VentureBeat Pulse Research examines enterprise AI infrastructure and compute: where organizations are in their deployment journey, what they run AI on today, how satisfied they are, what would make them switch, where they plan to evaluate their investments, and — most revealingly — how well they can measure and control the economics of the compute underneath it all. The central finding is a compute gap — the distance between how aggressively enterprises are investing in AI infrastructure and how little of its economics they can see. Only about one in five (21%) run AI in production at scale, yet spending intentions are outrunning that maturity: the single largest planned area enterprises plan to evaluate over the next year is AI-specialized clouds (45%), a layer almost none of these enterprises use today. Meanwhile the compute already in place runs cold — 83% report GPU utilization of 50% or less — and fewer than half (44%) can rigorously track what their AI compute costs. Enterprises are buying more infrastructure faster than they can account for what they already own. Enterprises are not settled on their infrastructure vendors, either: A clear majority (64%) plan to switch or add an infrastructure provider within twelve months, and 38% within the next quarter — unusually high churn intent for a category this foundational. When they choose, they choose on integration with the existing stack (41%) and total cost of ownership (35%), not on headline price: cost per million tokens is the deciding factor for just 8%. And the frontier constraint that will shape the next round of decisions — the shift from GPU compute to memory bandwidth as inference scales — is barely on the radar, with roughly one in five enterprises either unaware of it or yet to address it. Methodology VentureBeat fielded this survey as part of its ongoing Pulse Research series, this survey focused on enterprise AI infrastructure, compute, and inference economics. Responses are filtered to organizations with more than 100 employees (n=107; the survey’s smallest size band, 1–100 employees, is excluded), drawn from a single Q2 2026 (June) wave. Because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%. By organization size the sample concentrates in the mid-market: 101–250 employees (36%) and 251–1,000 (27%) lead, with 1,001–5,000 (22%), 5,001–10,000 (8%), and 10,001+ (7%) above them. By role it spans managers (38%), individual contributors (28%), VPs and directors (19%), and the C-suite (13%); on purchasing authority it is buyer-credible, with 45% final decision-makers and another 30% recommenders or influencers for AI solutions. Technology/Software is the largest industry at 26%, followed by Healthcare/Life Sciences (15%), Financial Services (13%), and Retail/E-commerce (12%). At 107 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It also skews toward the mid-market and toward earlier-stage adopters, so it is best read as the view from organizations actively building out AI infrastructure rather than from the largest hyperscale operators. Finding 1: Ambition outpaces production Only one in five run AI in production at scale We asked where organizations sit in their AI deployment journey. Most are still building toward production rather than operating at scale. The maturity curve is front-loaded. Three-quarters of enterprises (76%) are either experimenting or running only some workloads in production, and just 21% describe AI in production at scale. This matters for everything that follows: the infrastructure decisions in this report are being made largely by organizations still early in deployment, whose compute footprint — and whose costs — are about to grow. The evaluation and switching intentions in Findings 3 and 4 are the leading edge of that build-out, not the settled preferences of operators who have already found what works. Finding 2: Enterprises run on hyperscalers and model APIs The specialized GPU clouds barely register — today We asked which providers and platforms enterprises currently use to run their AI. The answer is a familiar one: the incumbents. The current stack is hyperscaler-and-API. Google Cloud leads at 48%, and the general-purpose clouds (Google, Microsoft, AWS, Oracle) together with the major model APIs (Gemini, OpenAI, Anthropic) account for essentially all current deployment. The specialized “neocloud” GPU providers that dominate AI-infrastructure headlines — CoreWeave, Lambda, Crusoe, Nebius and peers — register at or near zero among these enterprises today. Only 6% run their own on-prem GPU clusters and 4% a custom open-source stack. Enterprises are, for now, running AI on the providers they already buy from — which makes the evaluation intentions in Finding 3 all the more striking. (A note on reading these shares. As described in the methodology section, this sample is self-selected and skews mid-market, and this question counted every provider a respondent uses — an average of 2.1 selections each — so the figures measure presence in the stack rather than spending or primary status. A sample built this way will show a different provider mix than a spend-weighted census of the broader market; Google's strength here, for example, is consistent with its long-standing position among smaller enterprises building on AI. Read these shares as a portrait of what this AI-active cohort runs today, and treat gaps between these figures and industry-wide market share estimates as a property of the sample rather than a contradiction of either.) Finding 3: The next dollar goes to infrastructure they don’t yet run AI-specialized clouds top the evaluations list We asked where enterprises planned to evaluate AI infrastructure over the next 12 months. Their answers point away from the stack they run today. Here is the report’s sharpest tension. The single most-cited planned evaluation area — AI-specialized clouds, at 45% — is the very category almost none of these enterprises use today (Finding 2). Nearly a third (32%) intend to evaluate non-Nvidia accelerators, and 28% in next-generation Nvidia silicon; even decentralized compute networks (16%) and sovereign compute (11%) draw meaningful interest. Read against current usage, this is not incremental — it is the leading edge of a re-platforming. The direction-of-travel question tells the same story: every infrastructure approach is net-expanding, but specialized AI clouds carry the highest net momentum (+24), edging out even the hyperscalers (+22). Enterprises are preparing to move a meaningful share of AI compute off the general-purpose cloud. This continues a trend we saw in our April-May survey wave. Back then, usage of the AI-specialized clouds was equally marginal — CoreWeave at 3%, Lambda at 4%, Crusoe at 2% of enterprises. When we asked enterprises what change they planned in their AI infrastructure strategy over the next twelve months, the most-cited answer was moving workloads to specialized AI clouds, at 33%. Asked in April-May which emerging compute option they were most likely to evaluate AI-specialized clouds again drew the most responses. Two waves, two differently worded questions, one consistent picture: the type of cloud enterprises are most eager to assess is the type they have barely begun to use. Finding 4: A switching wave is building Six in 10 plan to change providers within a year — many within a quarter We asked whether and when enterprises plan to switch or add an infrastructure provider. Very few intend to stand still. For a category as foundational as compute, this is a remarkable amount of intended movement. Only 36% have no plans to change, meaning a clear majority (64%) intend to switch or add a provider within twelve months — and 38% within the next quarter alone. Where that interest points is telling: the providers drawing the most switching consideration are again the incumbents — Microsoft Azure and Google Cloud (33% each), OpenAI (30%), and Gemini (22%) — which suggests much of the near-term movement is reshuffling among the majors and consolidating spend rather than defecting to new entrants. The neocloud interest in Finding 3 is a 12-month evaluation thesis; the switching in the next quarter is mostly incumbents trading share. (Method note: Respondents who selected both "no plans to change" and a specific switching window are counted as switchers, on the logic that naming a timeframe is the more specific answer; three respondents were reclassified under this rule.) Finding 5: Nobody buys on token price Integration and total cost of ownership decide — not sticker price We asked what matters most when enterprises select an AI infrastructure provider. Headline price finished last. Enterprises do not buy AI infrastructure on pricing, which is the place vendors compete on hardest. Integration with the existing stack (41%) and total cost of ownership (35%) dominate, while the headline metric — cost per million tokens — is the deciding factor for just 8%, dead last. The pattern is coherent: buyers are optimizing for how a provider fits and what it truly costs to operate, not for the advertised unit rate. It also foreshadows Finding 7 — enterprises say TCO matters most, yet most cannot yet measure it rigorously. The stated priority and the measured capability are out of step. Finding 6: Expensive GPUs, idle most of the time 83% report GPU utilization of 50% or less We asked what share of their GPU capacity enterprises actually utilize. The answer is a well-known but rarely quantified inefficiency. Disclosure: Band percentages count every selection against all 107 qualified respondents; 14 respondents selected more than one band, so bands overlap. At the respondent level, 83 of the 100 GPU-operating enterprises reported utilization at or below 50% The compute already in place runs cold. Adding the bands at or below half capacity, 83% of enterprises that operate GPUs report utilization of 50% or less, and nearly half (49%) run at 25% or below. Only 12% clear the 50% mark, and a further 8% do not measure utilization at all. Idle accelerators are expensive accelerators, and this is the clearest single measure of the compute gap: enterprises are planning to buy more GPUs and specialized compute (Finding 3) while the capacity they already own sits substantially unused. The efficiency headroom in the current fleet is large — and largely unmeasured. Finding 7: Spending fast, measuring slowly Fewer than half rigorously track what their compute costs We asked whether enterprises can quantify the cost and return of their AI infrastructure spend, and how satisfied they are with what they run. Confidence in the ledger lags the spending. Measurement trails money. Fewer than half of enterprises (44%) rigorously track the cost and return of their AI compute; the majority track only partially (39%), cannot quantify it yet (20%), or have not prioritized it (6%). That gap is consequential given Finding 5, where total cost of ownership was the second-ranked buying criterion — enterprises are choosing providers on an economic basis they mostly cannot yet measure. Satisfaction with current infrastructure is moderately positive but not enthusiastic: on a five-point scale, overall satisfaction averages 4.0, with ease of implementation (3.8) and value for money (3.9) trailing slightly — the softness landing, tellingly, on cost. Enterprises are spending quickly and accounting slowly. Finding 8: The next bottleneck few are watching As inference shifts from compute to memory, the field scatters Finally, we asked how enterprises would address the emerging constraint in large-scale inference — the shift from GPU compute to memory, specifically KV-cache capacity. The responses reveal a frontier that is not yet a priority. The memory frontier is real but barely governed. Asked which approach they would rely on as the binding constraint in inference shifts from compute to memory bandwidth, enterprises scatter: Dell leads at 31%, Nvidia follows at 16%, and the rest fragments across storage vendors, open-source tooling, and model-level efficiency techniques. Most telling is that roughly one in five (18%) either do not recognize the constraint or have not begun to address it. For a shift that will reshape inference cost and architecture, this is an early and unsettled market — and, consistent with the measurement gap in Finding 7, one where many enterprises simply do not yet have a view. It is the next chapter of the compute gap, arriving before most have closed the current one. The bottom line: A compute gap that faster spending will widen, not close Organizations with more than 100 employees are investing in AI infrastructure faster than they can measure it. Most are still early in deployment, yet their spending intentions point past their current stack — toward specialized clouds and alternative accelerators almost none of them run today — and a clear majority intend to change providers within the year. They buy on integration and total cost of ownership rather than headline price, which is rational; the difficulty is that most cannot yet see those economics clearly. The visibility gap is concrete. The GPUs enterprises already own run at half utilization or less for the overwhelming majority, and fewer than half can rigorously track what their compute costs or returns. Satisfaction is decent but unenthusiastic, softest on value for money — the dimension hardest to judge without measurement. And the next constraint, the shift from compute to memory in large-scale inference, is arriving while most enterprises are still unaware of it. At 107 respondents in a single Q2 wave this is a directional read, skewed toward the mid-market and earlier-stage adopters — but the direction is consistent: the appetite to spend is running well ahead of the instrumentation to spend well. The compute gap is not a capacity problem that more hardware will solve on its own; it is, first, a problem of seeing what the hardware already costs. The open question for later waves is whether enterprises build that visibility before the re-platforming arrives — or buy the next layer of infrastructure as blind to its economics as the last. Based on survey responses from 107 qualified enterprise respondents (100+ employees), drawn from a single Q2 2026 (June) wave. Because this is one wave rather than a pooled multi-month sample, the results read cross-sectionally rather than as a month-over-month trend, and at 107 respondents this is a directional signal rather than a precise measurement — the sample is self-selected, skews mid-market, and leans toward earlier-stage adopters rather than the largest hyperscale operators. Respondents include managers, individual contributors, VPs/directors, and the C-suite, with buyer-credible purchasing authority, across Technology/Software, Healthcare/Life Sciences, Financial Services, Retail/E-commerce, and other industries. 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Across 157 enterprises, organizations are granting AI agents more autonomy while trusting the evaluations meant to gate that autonomy less. Half have already shipped an agent that passed their internal evaluations and then failed a customer in production; only one in twenty fully trusts automated evaluation today; and the most-cited weakness is that evaluations do not align with real-world outcomes. Yet two-thirds already allow, or are actively engineering toward, deploying agent changes to production on automated evaluation alone — with no human in the loop. The result is an evaluation gap — the distance between how much autonomy enterprises are handing their agents and how far they trust the tests that are supposed to catch the failures. This wave of VentureBeat Pulse Research examines how technical leaders measure agent performance: which reliability and evaluation platforms they use, how they select and trust them, what breaks in production, and how far they are willing to let agents run without a human in the loop. The central finding is an evaluation gap — the distance between the autonomy enterprises are granting their agents and the trust they place in the evaluations meant to govern it. Half of organizations (50%) have, in the past year, deployed an agent or LLM feature that passed their internal evaluations and then caused a customer-facing failure, and a quarter have seen it happen more than once. Trust in the tests themselves is thin: only 5% say they fully trust automated evaluation today, and the single most-cited limitation is that evaluations align poorly with real-world outcomes (29%). Enterprises are discovering that a passing eval is not the same as a working agent. What makes the gap consequential is the direction of travel. Two-thirds of organizations (66%) already permit fully automated, zero-human-in-the-loop deployment for low-risk agents (34%) or are actively engineering their pipelines to allow it within twelve months (33%). At the same time, the evaluation stack that would have to earn that trust is fragmented and immature: the most common primary tools are the model providers’ native evals, tied with having no dedicated tooling at all (17% each); and only about a quarter of enterprises run real-time quality checks on live production traffic. The autonomy is arriving faster than the assurance. Methodology VentureBeat fielded this survey as part of its ongoing Pulse Research series, this survey — the Agentic Reliability & Evals tracker — focused on how technical leaders evaluate agent performance and reliability. Responses are filtered to organizations with 100 or more employees (n=157), drawn from a single survey in June 2026; because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Where questions were multiple-select, those shares can sum to more than 100%. By role the sample is senior and buyer-credible: 38% are final decision-makers for AI purchases and another 34% recommenders or influencers. Product and program managers (15%), consultants and advisors (10%), directors of engineering/IT (8%), and CIOs/CTOs/CISOs (8%) lead the named titles, alongside a large “Other” function (37%). By organization size the sample is mid-market-weighted: 100–499 (37%) and 500–2,499 (27%) employees lead, with 2,500–9,999 (20%), 10,000–49,999 (10%), and 50,000+ (6%) above them. Technology/Software is the largest industry at 23%, followed by Retail/Consumer (15%), Healthcare/Life Sciences (12%), and Manufacturing (10%). At 157 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It skews toward the mid-market, so it is best read as the view from organizations actively standing up agent evaluation practices rather than from the largest operators. Note: This survey was rebuilt for the June wave from the earlier “LLM observability and evaluations” survey; because the questions and sample differ, no comparisons are made to the April–May data. Finding 1: A passing eval is not a working agent Half have shipped an agent that passed evals, then failed a customer We asked whether, in the past 12 months, organizations had deployed an agent or LLM feature that passed their internal evaluations but then caused a customer-facing failure. Half of those that run evaluations had. This is the report’s defining number. Half of organizations (50%) have shipped an AI feature that cleared their internal evaluations and then failed in front of a customer — an incorrect output, a broken workflow, or a quality incident — and a quarter have seen it happen more than once. Only 36% report no such failure, and the remainder either run no pre-deployment evaluations (8%) or don’t track the root cause closely enough to know (6%). The failure is precise and expensive: the evaluation said the agent was ready, and it was not. Everything that follows — how enterprises trust their evals, what they monitor, and how much autonomy they grant — is shaped by this experience. Finding 2: Almost no one fully trusts automated evaluation The top complaint: Evals don't match real-world outcomes We asked which limitation most reduces trust in automated agent evaluations today. Only a sliver of enterprises had no complaint at all. Trust in automated evaluation is scarce, and specific. Only 5% of organizations say they fully trust automated evaluation as it stands — meaning 95% name a limitation that holds them back. The most common, at 29%, is the one that most directly explains Finding 1: evaluations align poorly with real-world outcomes, passing agents that later fail. Bias or inconsistency (21%) and a lack of explainability (18%) follow — enterprises cannot always tell why an evaluation reached its verdict — and 17% cite data-leakage or privacy concerns in the evaluation process itself. The tests meant to certify agents are not yet trusted to certify them, which is precisely why the autonomy trajectory in Finding 3 is so striking. Finding 3: The autonomy ceiling is rising anyway Two-thirds already allow, or are building toward, zero-human deployment We asked whether organizations would let an autonomous agent deploy a code or system change to production on automated evaluation results alone, with no human-in-the-loop validation. The trajectory runs straight through the trust gap. Here is the paradox at the heart of the report. Even though almost no one fully trusts automated evaluation (Finding 2), two-thirds of organizations (66%) either already allow zero-human-in-the-loop deployment for low-risk agents (34%) or are actively engineering their pipelines to permit it within a year (33%). Only 22% rule it out for the foreseeable future. The direction is unambiguous: enterprises are moving to let evaluations gate production autonomously — removing the human check — at the same moment they say those evaluations don’t reliably match reality. The autonomy ceiling is rising faster than the assurance beneath it, which is the mechanism by which the false-confidence failures of Finding 1 will scale rather than shrink. Notably, the autonomy bet is not just a small company phenomenon. Splitting the sample by company size, larger enterprises are slightly further down the path toward zero human review than smaller companies (70% versus 64%) and slightly more likely to have shipped an evaluation-passing agent that then failed a customer (54% versus 48%). The assumption that large, regulated organizations are holding the human in the loop longest is, in this sample, backwards. To be sure, these are directional figures, since the survey was not a huge sample — 57 respondents from companies with 2,500+ employees and 100 from companies smaller than that. Finding 4: The evaluation stack is fragmented and provider-led Provider-native evals lead — tied with no dedicated tool at all We asked which agent reliability or evaluation platform enterprises primarily use today. The market has no clear leader — and a large share has nothing dedicated. The evaluation layer is early and unconsolidated. Provider-native tooling leads — OpenAI’s native evals and traces (17%) and Anthropic’s Claude Console evals (13%) together outweigh any independent platform — but it is tied at the top by a striking answer: 17% of enterprises use no dedicated agent-evaluation tooling at all, a notable gap for organizations shipping agents to customers. The specialist evaluation vendors — DeepEval (12%), Braintrust (8%), LangSmith, Weave, Promptfoo, Langfuse, Arize — are scattered across single to low double digits, and 11% have built their own. No independent platform has yet become the category standard, which leaves most enterprises evaluating agents with provider-native tools, home-grown scripts, or nothing. Finding 5: Production monitoring rarely watches output quality Only a quarter run real-time quality checks on live traffic Production monitoring for an AI agent can watch two very different things. It can watch whether the system is functioning — is the agent up and responding, did each request complete, how fast, at what cost, with any errors. Or it can watch whether the agent's output is correct — automated checks that evaluate the content of each answer as it goes out: did the agent give the right answer, take the right action, stay within policy. The distinction matters because a confidently wrong answer is invisible to the first kind of monitoring: the request completes, the response is fast, no error is thrown, and every functioning-metric reads healthy. We asked organizations which kind their live production monitoring is built for today. Grouped by what is actually being watched, the split is stark: 51% of organizations monitor only whether the agent is functioning, while 23% monitor whether its answers are right. Counting the ad-hoc reviewers and the don't-knows, roughly three-quarters of organizations run no automated, real-time evaluation of output correctness in production — they can see that the system is up and what it costs, and they are taking the correctness of its answers on faith. That blind spot is the runtime counterpart to the pre-deployment gap in Finding 1: the same organizations engineering the human out of the deployment decision mostly cannot see, in real time, when the deployed agent starts getting things wrong. Finding 6: Bought on cost, measured on consistency Price and integration drive selection; evaluation consistency is the goal We asked what most influenced enterprises’ choice of an evaluation vendor, and what they treat as their primary measure of success. Both answers are pragmatic. Enterprises buy evaluation tooling on economics and trust it on repeatability. Cost of evaluations (28%) narrowly leads selection, just ahead of ease of integration (27%) and evaluation accuracy (24%) — breadth of observability (13%) and vendor roadmap (4%) matter far less. On what success looks like, more than a third (36%) name evaluation consistency — getting the same verdict on the same behavior every time — well ahead of speed of experimentation (19%), reduction in failures (18%), production visibility (13%), and compliance (11%). The emphasis on consistency is telling: before enterprises can trust an evaluation’s verdict, they need it to be stable — the very property whose absence (bias and inconsistency) ranked among the top trust limitations in Finding 2. Satisfaction with current tooling is only moderate, averaging 3.8 on a five-point scale across overall satisfaction, ease of implementation, and value for money. Finding 7: The next dollar goes to humans and observability Investment is flowing to oversight, not just automation We asked which reliability and evaluation investment will grow most over the next year. The money is going toward watching agents more closely — including with people. The second-largest planned investment — behind only production observability — is human review workflows, at 26%. Read against Finding 1, that is the report's quietest contradiction: at the same moment two-thirds of enterprises are engineering the human out of the deployment decision, more of them plan to grow spending on human reviewers (26%) than on the automated evaluation pipelines (16%) that would replace them. The zero-human trajectory and the human-review budget are rising in the same companies at the same time. Indeed, only 8% report that their budget is not increasing. Taken together, enterprises are hedging: building toward autonomy while spending to watch agents more closely and keep humans available for the calls that automated evaluation cannot yet be trusted to make. Finding 8: A tooling reshuffle is coming Nearly two-thirds plan to adopt or switch platforms within a year We asked whether enterprises plan to adopt a new, additional, or replacement evaluation platform, and which they are considering. Few intend to stand pat. The evaluation market is wide open. While 36% have no plans to change, a clear majority (64%) intend to adopt a new, additional, or replacement platform within twelve months, and 31% within the next quarter. The consideration set points where current usage is thinnest: Confident AI’s DeepEval leads what enterprises are evaluating (20%), ahead of OpenAI’s native evals (13%) and Braintrust (9%) — the open-source specialists drawing more interest than their present footprint. Given that so many enterprises today rely on provider-native tools or nothing at all (Finding 4), this is less a defection than a first real wave of tooling adoption — the moment the evaluation layer starts to consolidate. Which platforms earn that trust, in a market where almost no one trusts automated evaluation yet, is the open question this series will keep tracking. The bottom line: An evaluation gap that autonomy will widen, not close Organizations with 100 or more employees are granting AI agents more independence than they trust their evaluations to support. Half have already shipped an agent that passed its evals and then failed a customer; almost none fully trust automated evaluation, chiefly because it doesn’t match real-world outcomes; and most watch production for uptime and cost rather than for whether the agent’s answers are right. Yet two-thirds already allow, or are actively building toward, deploying to production on automated evaluation alone. The vendor market is early and unsettled: the most common primary evaluation tools are provider-native evals, tied with no dedicated tooling at all, and a clear majority plan to adopt or switch platforms within the year. Encouragingly, the next dollar is going to observability and — pointedly — human review, suggesting enterprises sense the gap even as they engineer past it. At 157 respondents in a single wave this is a directional read, skewed toward the mid-market — but the direction is clear: autonomy is being granted on the strength of evaluations that the people granting it do not yet trust. The evaluation gap is not a coverage problem that more tests alone will close; it is a problem of evaluations that reflect reality and can be trusted to gate it. The open question for later waves is whether assurance catches up to autonomy — or whether the false-confidence failures move from customer incidents into changes that deploy themselves. Based on survey responses from 157 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. This is a directional read rather than a precise measurement — the sample is self-selected, not a probability sample, and skews toward the mid-market. Respondents include product and program managers, consultants and advisors, directors of engineering/IT, and CIOs/CTOs/CISOs, among other functions, across technology/software, retail/consumer, healthcare/life sciences, manufacturing, and other industries. Maestro News, gelişmeyi Maestro Dev ekseninde — yazılım, yapay zekâ, bulut ve ürün teslimatı — özgün Türkçe dilde yeniden çerçeveliyor.
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Across 101 enterprises, agent orchestration is consolidating onto model-provider platforms — Anthropic’s Claude leads by a wide margin — chosen for the gravity of the underlying model and judged on reliable multi-step execution. But the ambition runs well ahead of the reality: most deployed “agents” are still chatbot wrappers, the control plane enterprises expect is deliberately hybrid to avoid lock-in, and real-time fiscal control over token burn remains the exception. This wave of VentureBeat Pulse Research examines enterprise agent orchestration: which platforms enterprises run on, what drives the choice, what they optimize for, how they expect agent control to be structured, and — most revealingly — how orchestrated their deployed “agents” actually are and how tightly they control the cost of running them. The central finding is a gap between orchestration ambition and orchestration reality. Enterprises are consolidating fast onto the major model platforms: Anthropic’s Claude is the primary platform for 40%, more than double any rival, followed by Microsoft (18%) and OpenAI (13%). The choice is driven by “model gravity” — native alignment with a state-of-the-art base model (21%) — and success is judged by reliable, multi-step execution (task completion reliability 32%, multi-step workflow management 28%). Yet asked to assess their portfolios honestly, 71% say a quarter or fewer of their deployed “agents” are true multi-step orchestrated workflows rather than single-prompt chatbot wrappers, and only 10% have crossed the halfway mark. The orchestration layer is being built well ahead of the orchestrated portfolio it is meant to run. That gap shapes the architecture enterprises are putting in place. By the end of 2026 a clear majority (51%) expect a hybrid control plane — provider-native plus external orchestration — and only 6% expect to hand control to a provider-managed service, because vendor lock-in (35%) is the risk they fear most if control lives inside a model provider. Investment follows the build-out: agent workflow tooling leads the spend (34%), with security and permissions enforcement (25%) behind. And fiscal control lags throughout — more than a quarter (27%) have no real-time way to stop a runaway agent before the bill arrives. Methodology VentureBeat fielded this survey as part of its ongoing Pulse Research series, this instrument focused on enterprise agent orchestration. Responses are filtered to organizations with 100 or more employees (n=101), drawn from a single June 2026 wave; because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. By organization size the sample is spread evenly across the enterprise bands: 100–499 employees, 2,500–9,999, and 50,000+ (21% each), with 10,000–49,999 and 500–2,499 (19% each). By role it is senior and buyer-credible: product and program managers (15%), CIO/CTO/CISO (13%), consultants and advisors (13%), and a spread of data, AI, and engineering directors and VPs, with an “Other” function at 18%. On purchasing, 81% are recommenders, influencers, or final decision-makers for AI solutions (66% recommender/influencer, 15% final decision-maker). Technology/Software is the largest industry at 44%, followed by Financial Services (17%) and Healthcare/Life Sciences (8%). At 101 respondents the sample is robust enough to read directionally with reasonable confidence, though it remains self-selected and is not a probability sample. Finding 1: Orchestration runs on model-provider platforms Anthropic’s Claude leads; open frameworks are marginal We asked which agent orchestration platform enterprises primarily use today. The answer concentrates on the major model providers — and on one in particular. A note on reading these shares. As described in the methodology section, the respondents are self-selected, and this question asked them for a single primary platform — so the figures measure which platform leads each enterprise's deployment, within a self-selected audience of AI-active technical decision-makers. A sample built this way can diverge substantially from spend-weighted market measures, and each VB Pulse survey draws its own sample with its own company-size mix, so vendor figures should not be compared across our surveys either. Read these shares as a portrait of where this cohort has placed its primary orchestration bet today, rather than as market share. The model platforms dominate. Anthropic, Microsoft, OpenAI, Google, and Amazon together account for roughly 80% of deployments (81 of 101), while the open frameworks (LangChain/LangGraph) and custom in-house builds that anchor engineering discussion sit in single digits. Anthropic’s lead — 40%, more than double the next platform — mirrors the “model gravity” selection logic in Finding 2: enterprises are choosing the orchestration layer that comes with the model they want to build on. As with the security vendors in the prior agent-security wave, the tools that define the category in technical circles are not yet where enterprise deployment concentrates. A small 3% are not orchestrating at all. Respondents rate the platforms they run at 3.94 out of 5 overall (109 answered), with “value for money” specifically at 3.94 and “ease of implementation” the weakest score, at 3.85 — placing orchestration near the bottom of our five-tracker satisfaction range, ahead of only evaluation tooling. A rating just under 4 out of 5, from users of whom 96% plan to change their orchestration approach within the year, reads as provisional acceptance: the platforms work well enough to run today, and not well enough to stop the search for something better. The ratings sit alongside near-universal intent to change; this is a layer enterprises tolerate more than they love. Finding 2: Model gravity drives platform selection The base model, not the tooling, decides the platform We asked what most influenced the orchestration platform choice. The single largest factor is the pull of the underlying model — though flexibility and ease of development follow close behind. Model gravity leading is the selection-side explanation for Anthropic’s platform lead: enterprises pick the orchestration environment closest to the frontier model they have standardized on. But the next tier complicates the picture — flexibility across models and tools (17%) and ease of development (17%) say enterprises also want to avoid being trapped by that choice, foreshadowing the lock-in fear in Finding 6. Security and permissions (14%) and total cost of ownership (11%) round out a pragmatic buying logic. Performance (latency/memory) sits last at 4%, a reminder that at this stage of adoption the binding constraints are model fit and optionality, not raw speed. Finding 3: The job is reliable multi-step execution Enterprises just orchestration by whether it completes the work We asked what enterprises optimize for — their primary success metric for orchestration. Reliability and multi-step workflow management dominate; developer- and user-facing metrics trail. Task completion reliability (32%) and multi-step workflow management (28%) together account for 59% of responses (60 of 101): orchestration succeeds, in the enterprise view, when it reliably carries a task through multiple steps to completion. Developer productivity (17%) matters but is secondary — the inverse of its prominence in framework discussion — and end-user experience (9%) is a minor concern, consistent with orchestration being an internal execution problem rather than a UX one. This reliability-first standard is exactly what makes the Chatbot Trap finding so pointed: enterprises define success as dependable multi-step execution, yet most of their deployed “agents” do not yet do multi-step work at all. The trap is not evenly distributed. Splitting the sample by organization size, 77% of smaller enterprises say a quarter or fewer of their agents do true multi-step work, against 62% of larger ones. Larger enterprises are meaningfully further into genuine multi-step deployment; the chatbot trap is, directionally, a mid-market condition. Finding 4: Consolidate, productionize, and build in-house Three strategic moves are nearly tied for the year ahead We asked what major change enterprises anticipate in their orchestration strategy over the next 12 months. Three moves cluster at the top, almost evenly split. The top three — building in-house control (25%), standardizing on one framework (24%), and moving agents from sandbox to production (23%) — are statistically indistinguishable and tell a single story: enterprises are moving from experimentation to operational consolidation. They want fewer frameworks, more production exposure, and more ownership of the control layer; only 4% expect no change. The appetite for custom in-house control planes is notable alongside the platform concentration in Finding 1 — enterprises are standardizing on model-provider platforms while simultaneously planning to wrap them in control logic they own, the hybrid posture that Finding 6 makes explicit. Finding 5: Nearly seven in 10 plan to switch — and the biggest group of movers has no shortlist The strategic change enterprises anticipate (previous finding) comes with vendor motion attached. Asked whether they plan to adopt a new, additional, or replacement agent orchestration platform in the next twelve months, more respondents are moving here than in any other layer we track. Asked which platforms they are considering, the most common answer among those in motion is none yet: 29% of all respondents are evaluating without a shortlist, the largest single response after "not considering a change." Among named candidates, OpenAI leads at 16%, followed by LangChain/LangGraph at 12% and Anthropic at 7% — and notably, the independent frameworks draw roughly double their current usage footprint in forward consideration, the same pattern our security tracker found for specialist vendors. Read with this report's concentration and lock-in findings, the picture completes itself: the major model-platform providers hold roughly four-fifths of today's primary usage, vendor lock-in has become the leading fear, 96% anticipate a strategic change — and now the purchase intent to act on all of it, with the largest bloc of buyers still undecided. The most concentrated layer of the agentic stack is also, as of June, the least settled. Finding 6: Investment flows to workflow tooling Tooling and permissions lead the spend; monitoring trails We asked which orchestration-related investment will grow most next year. Agent workflow tooling leads, with security and permissions enforcement behind. Workflow tooling leading (34%) is the budget-side expression of the reliability-and-multi-step priority in Finding 3: the money is going to the machinery that strings steps together dependably. Security and permissions enforcement (25%) and scaling infrastructure (20%) follow — the investments required to take agents from sandbox into production, the strategic move in Finding 4. Monitoring and debugging draws a smaller 11%, with another 11% reporting flat budgets. The weight on tooling, permissions, and scaling over pure observability signals that enterprises are spending to build and harden orchestration, not merely to watch it run. Finding 7: The control plane will be hybrid — and lock-in is why Enterprises expect to split control between providers and their own layer We asked where enterprises expect the primary control plane for agents to live by the end of 2026, and what worries them most if that control sits inside a model-provider platform. A clear majority expect a hybrid model — and vendor lock-in is the reason. Hybrid control is the dominant expectation by a wide margin (51%), and only 6% expect to hand control to a provider-managed service outright. Read together, the hybrid, custom, and externally-abstracted options — every architecture that keeps control at least partly outside the provider — sum to 88% (89 of 101). The reason surfaces directly when we asked about the risk of provider-resident control: vendor lock-in leads at 35% (35 of 101), ahead of security and permissioning limitations (28%) and inflexibility across models and tools (21%). The pattern echoes the prior wave’s “don’t trust the model to police itself” posture — here, enterprises will build on a provider’s platform but decline to be governed entirely by it. The hybrid control plane is the architectural hedge against the lock-in they most fear. The June figure asserting a preference for a hybrid control plane marks movement from earlier. In the April–May survey (n=145), only 34% expected a hybrid control plane, and a greater number (12%) expected to hand control fully to a provider-managed service. These two snapshots don’t yet measure a confirmed longitudinal trend — but the direction of the conversation is unambiguous: toward keeping control. Lock-in is also a new arrival as a top concern. In the April–May wave, the leading concern was security and permissioning limitations (32%), with lock-in second at 24%; by June the two had traded places. The worry about provider platforms appears to be maturing from whether they can be secured to whether they can be replaced. Finding 8: The chatbot trap — most “agents” aren’t agents yet Enterprises admit most deployments are still chatbot wrappers We asked enterprises to assess their portfolios honestly: what share of their deployed “agents” are true multi-step orchestrated workflows versus simple single-prompt chatbot wrappers. The answer is the defining finding of this wave. This is the gap at the center of the report. Combining the bottom two bands, 71% of enterprises (72 of 101) say a quarter or fewer of their deployed “agents” are genuinely orchestrated — and just 10% (10 of 101) have crossed the halfway mark. The ambition documented in the earlier findings — model-provider platforms, reliability-first success metrics, production rollouts, a deliberate control architecture — runs well ahead of the deployed reality, which remains overwhelmingly single-prompt assistants dressed as agents. This is less a contradiction than a roadmap: the platforms, budgets, and strategies are being put in place precisely because the orchestrated portfolio is still so thin. The open question for later waves is how fast the reality closes on the ambition. Finding 9: Fiscal control is still reactive Only a minority can stop a runaway agent before the bill arrives Finally, we asked how enterprises enforce fiscal control over agent token consumption — the risk that an autonomous loop exhausts a budget before anyone intervenes. Most rely on native caps or after-the-fact monitoring; real-time programmatic control is the exception. More than a quarter of enterprises (27%) admit they have no real-time, programmatic way to stop an agent before a budget-breaking bill arrives — they learn of it from the logs afterward. Another 32% lean entirely on the native caps and throttles built into their primary platform, a control only as good as the provider’s tooling and one that ties back to the lock-in concern of Finding 6. The enterprises building custom gateways (23%) or exploiting cross-model routing to arbitrage cost (19%) are the ones treating token burn as an engineering problem to be controlled deterministically. As with orchestration maturity, fiscal control is an area where the operational reality lags the ambition: agents are moving toward production faster than the cost-control plane around them is being built. It’s worth noting, a split appears according to company size: roughly one in three enterprises under 2,500 employees (34%) exercises only reactive control of agent spend, against 20% of larger enterprises — directional figures, but consistent with the chatbot-trap split. The mid-market is running the least mature agents on the least instrumented budgets. The bottom line: The layer is real; most of the agents aren't yet Organizations with 100 or more employees describe an orchestration strategy that is consolidating quickly and maturing slowly. They are standardizing — for now — on model-provider platforms, which collectively hold roughly four-fifths of primary usage, chosen for the gravity of the underlying model, and they judge success by reliable multi-step execution. Investment is flowing to workflow tooling and permissions, the strategy is to consolidate frameworks and push agents into production, and the control plane they expect is deliberately hybrid, because vendor lock-in is the risk they fear most. But the standardization is provisional: 68% plan to adopt a new, additional, or replacement orchestration platform within twelve months — the highest switching intent of any layer we track — and the largest group of those movers has not yet shortlisted a candidate. Today's concentration describes where enterprises are, and visibly does not describe where they intend to stay. But the honest self-assessment punctures the ambition. Seventy-one percent say a quarter or fewer of their deployed "agents" are truly orchestrated, only 10% are past the halfway mark, and more than a quarter cannot stop a runaway agent in real time. The orchestration layer — the platforms, the budgets, the control architecture — is being built ahead of the orchestrated portfolio it is meant to run. At 101 respondents in a single June wave this reads as a clear directional signal rather than a precise measurement: enterprises have decided how they want to orchestrate agents well before most of their agents are doing anything an orchestration layer is for. The questions for subsequent waves are whether the deployed reality closes the gap on the ambition — and, with nearly seven in ten buyers in motion and most of them undecided, which platforms the settled stack finally lands on. Based on survey responses from 101 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. Because this is one wave rather than a pooled multi-month sample, results read directionally rather than as a confirmed trend. Respondents include product and program managers, CIOs, CTOs and CISOs, consultants and advisors, and directors and VPs of data, AI, and engineering, across Technology/Software, Financial Services, Healthcare, and other sectors. Maestro News, gelişmeyi Maestro Dev ekseninde — yazılım, yapay zekâ, bulut ve ürün teslimatı — özgün Türkçe dilde yeniden çerçeveliyor.
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Across 107 enterprises, AI agents are being given real access to systems and data while the controls meant to contain them lag behind. More than half have already had a confirmed agent security incident or a near-miss; only about a third give every agent its own scoped identity, and most agents still share credentials; and only three in ten isolate their highest-risk agents. The security stack is overwhelmingly borrowed from the model providers and hyperscalers rather than purpose-built for agents, spending remains a thin slice of the security budget, and enterprises are evenly split on whether their defenses are keeping pace with AI-enabled attackers. The result is an agent security gap — autonomous agents proliferating faster than the identity, isolation, and enforcement controls needed to hold them. This wave of VentureBeat Pulse Research examines how enterprises secure their AI agents: what tooling they run, how they manage agent identity and isolation, what has already gone wrong, how much they spend, and whether they believe their defenses are keeping pace with AI-enabled attackers. The central finding is an agent security gap — the distance between the autonomy enterprises are granting their agents and the controls in place to contain them. More than half of organizations (54%) have already experienced a confirmed agent security incident (18%) or a near-miss caught before harm (36%). The structural weakness beneath those numbers is identity: only about a third (32%) give every agent its own scoped, managed identity, while the rest report that some agents share credentials or that agents mostly run on shared API keys and human or service-account credentials. When agents share credentials, a single compromised or over-permissioned agent carries a wide blast radius — and only three in ten enterprises (30%) isolate their highest-risk agents in sandboxes to bound that radius. What makes the gap notable is how comfortable enterprises are inside it. The security stack is overwhelmingly provider-native — OpenAI’s guardrails (51%), Google’s and Microsoft’s cloud controls, and Anthropic’s managed-agent controls dominate, while the dedicated agent-security specialists barely register — and satisfaction with that borrowed stack is high, averaging 4.2 out of 5. Yet spending remains a thin slice of the security budget, only a third of enterprises believe their AI defenses are ahead of AI-enabled attackers, and a clear majority plan to change tooling within the year. Enterprises are satisfied with controls they are simultaneously preparing to replace. Methodology VentureBeat fielded this survey as part of its ongoing Pulse Research series, this instrument focused on enterprise agent security — the tooling, identity, isolation, and enforcement controls organizations use to secure autonomous AI agents. Responses are filtered to organizations with more than 100 employees (n=107; the survey’s smallest size band, 1–100 employees, is excluded), drawn from a single June 2026 wave. Because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%. By role the sample is senior and buyer-credible: 45% are final decision-makers for AI purchases and another 30% recommenders or influencers. Managers (43%), individual contributors (24%), VPs and directors (15%), and the C-suite (11%) make up the seniority mix. By organization size the sample is mid-market-weighted: 251–1,000 (42%) and 101–250 (25%) employees lead, with 1,001–5,000 (19%), 5,001–10,000 (8%), and 10,001+ (7%) above them. Technology/Software is the largest industry at 23%, followed by Manufacturing (15%), Retail/E-commerce (14%), and Healthcare/Life Sciences (13%). At 107 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It skews toward the mid-market, so it is best read as the view from organizations actively standing up agent security rather than from the largest operators. Satisfaction ratings are computed on the respondents who answered each rating question; the overall satisfaction score reflects 82 of the 107 qualified respondents. Finding 1: The incidents are already here More than half have had an agent security incident or near-miss We asked whether organizations had experienced an agent security incident — a confirmed breach, or a near-miss caught before harm. Most that run agents in production had. This is the report’s defining number. More than half of organizations (54%) have already had an agent security event — 18% a confirmed incident and 36% a near-miss caught before it caused harm. Only 42% report nothing, and a small remainder either run no agents in production or don’t track such events. That so many report near-misses rather than only confirmed incidents is telling: enterprises are catching problems, but they are catching them close to the edge. The controls examined in the rest of this report — identity, isolation, enforcement — are what determine whether the next near-miss stays a near-miss. Exposure scales with company size, but containment does not. The incident-or-near-miss rate rises from 49% in the mid-market (companies with 101-1,000 employees) to 63% at larger enterprises (above 1,000 employees), while sandbox isolation of high-risk agents falls from 35% to 20%, and satisfaction with security tooling drops from 4.36 to 3.97. The organizations running the most agents across the most systems carry the most incidents and the least of the one control that bounds an incident's blast radius. Finding 2: The identity gap Only a third give every agent its own scoped identity We asked how enterprises manage the identity of their AI agents — whether each agent has its own credentials, or agents share them. Full per-agent identity is the exception. Rolled together, the overlapping answers show 69% of enterprises (74 of 107) with credential sharing somewhere in the agent fleet. Identity is the structural weakness beneath the incidents. Only about a third of enterprises (32%) give every agent its own scoped, managed identity — the precondition for least-privilege access and clean attribution. Nearly half (48%) say some agents have scoped identities but many still share credentials, and another 32% say agents mostly run on shared API keys or borrowed human and service-account credentials. (Respondents could describe more than one pattern across their agent fleet, so these overlap.) The consequence is direct: when agents share credentials, an over-permissioned or compromised agent can act with far more reach than intended, and forensics after an incident cannot cleanly tell which agent did what. The non-human identity problem — giving every agent its own governed identity — is the single largest unfinished piece of enterprise agent security. Moreover, a company’s agent credential posture is correlated with incidents. Organizations with credential sharing anywhere in the fleet were hit — with an incident or a near-miss in the past twelve months — at 63.5% (47 of 74). Organizations where every agent carries its own scoped identity were hit at 40.9% (9 of 22). The fully-scoped group is small, so for now the relationship is an association rather than proven causation, and the gap is concentrated in the mid-market — but within a single survey, a twenty-three point difference in incident rate suggests significance. Finding 3: Observe and enforce, but rarely isolate Only three in 10 sandbox their highest-risk agents We asked what an organization’s agent security posture looks like in practice — whether they observe, enforce, isolate, or some combination. The control that bounds damage is the least common. Monitoring and enforcement are reasonably common; containment is not. Roughly half of enterprises observe agent activity (47%) or enforce scoped permissions at runtime (49%), but only 30% isolate their highest-risk agents in sandboxes that bound the blast radius when the other controls fail. That ordering is backwards from a defense-in-depth standpoint: observation tells you what happened, enforcement tries to prevent it, but isolation is what limits the damage when prevention fails — and it is the control enterprises have adopted least. Combined with the identity gap in Finding 2, the picture is of agents that are watched and permissioned but rarely boxed in, which is precisely the configuration in which a single failure propagates. Finding 4: Security runs on borrowed, provider-native controls Guardrails from OpenAI, Google and Microsoft dominate; specialists barely register We asked which agent security tooling enterprises use, and which is their primary layer. The answer favors the model providers and hyperscalers over the dedicated security vendors. Enterprises are securing agents with tools that came bundled with their models and clouds. OpenAI’s guardrails lead at 51%, followed by Google’s and Microsoft’s cloud-native controls and Anthropic’s managed-agent controls — and when asked to name their single primary security layer, 82% name one of these provider-native offerings. The purpose-built agent-security category — Palo Alto’s Prisma AIRS, CrowdStrike, Cisco AI Defense, Zenity, HiddenLayer, Check Point’s Lakera, Okta for AI Agents, non-human identity platforms — barely registers, each in the low single digits, and only 5% run no dedicated tooling at all. As with retrieval and evaluation elsewhere in this series, the provider bundle is winning the default: enterprises reach first for the guardrails their platform ships, and the independent security layer that would address the identity and isolation gaps has not yet been adopted at scale. The provider-default pattern is consistent across both Q2 survey waves. In April–May (n=110), usage was led by the same names — OpenAI's controls at 26%, Azure at 15%, AWS at 14%, Google at 12% — with every dedicated agent-security specialist at 3% or below and one in ten using no dedicated tooling at all. The common finding from the two surveys: Enterprises are defaulting to the solutions provided by the platform they’re using, and the specialist category vendors have yet to become big players here. (A note on reading these shares. As described in the methodology section, the respondent sample is self-selected and skews mid-market, and the usage question counted every vendor or approach a respondent has in place — so the figures measure presence in the security stack rather than spending or exclusivity. Individual vendor percentages therefore carry all the usual sample caveats. The structural pattern, however, held across both Q2 waves on two differently worded questions: provider-native and hyperscaler controls lead, and dedicated agent-security specialists remain in low single digits. Read the individual shares loosely and the pattern with confidence.) Finding 5: And enterprises are comfortable with it Satisfaction is high, even as incidents mount and identity lags We asked how satisfied enterprises are with their current agent security tooling. The comfort is notably out of step with the exposure documented above. Satisfaction with agent security tooling is high — 4.2 out of 5 overall, and 4.1 for value for money — among the most positive readings in this series. That is the striking part: enterprises are highly satisfied with a stack that is mostly borrowed provider guardrails, even though more than half have already had an incident or near-miss and only a third give their agents scoped identities. The comfort appears to rest on the convenience and low friction of provider-native controls rather than on demonstrated containment. It is a false comfort in the making — the same enterprises expressing satisfaction are, as Finding 8 shows, a clear majority planning to change tooling within the year, which suggests the confidence is thinner than the score implies. Finding 6: Budgets haven’t caught up Most spend under a tenth of the security budget on agents We asked what share of the security budget enterprises allocate to securing AI agents. For a fast-emerging risk, the allocation is modest. Spending on agent security is still a thin slice. The most common allocation is 6–10% of the security budget (46%), and a third of enterprises (34%) spend 5% or less; only a quarter (24%) devote more than a tenth. Given the incident rate in Finding 1 and the identity and isolation gaps in Findings 2 and 3, the budget looks like a lagging indicator — the risk has arrived faster than the funding to address it. The enterprises spending more than a tenth of their security budget on agents are a distinct minority, and they are likely the ones building the scoped-identity and isolation controls the rest have not. Finding 7: The arms race is even, at best Only a third think their AI defenses are ahead of AI-enabled attackers We asked how enterprises assess the balance between their AI-enabled defenses and AI-enabled attackers. Confidence is far from settled. Enterprises are split on whether they are winning. Only about a third (35%) believe their AI-enabled defenses are ahead of AI-enabled attackers; the rest are less sure — 32% call it roughly even, 21% think attackers are ahead, and another 21% say it is too early to tell. Taken together, a clear majority (53%) rate the balance as even or tilted toward the attacker. That uncertainty sits uneasily beside the high satisfaction of Finding 5: enterprises are content with their tooling yet unconvinced it is winning the contest it exists to win. In a domain where the offense is also compounding with AI, an even race is not a comfortable place to be. Finding 8: A security reshuffle is coming Nearly six in 10 plan to adopt or switch tooling within a year We asked whether enterprises plan to adopt a new, additional, or replacement agent security solution, and which they are considering. Few intend to stand pat. The security stack is not settled. While 41% have no plans to change, a clear majority (59%) intend to adopt a new, additional, or replacement agent security solution within twelve months, and 29% within the next quarter — a strong signal that, high satisfaction notwithstanding, enterprises know the current stack is provisional. Incidents are what start the buying cycle. Among organizations that have been hit, 42.1% plan to adopt, add, or replace agent security tooling within the next ninety days, against 14.0% of organizations with no incident — and after a confirmed incident it becomes majority behavior, at 52.6%. Getting hit also changes the threat assessment: 33.3% of hit organizations say AI-armed attackers are ahead of their defenses, against 8.0% of the unhit. Experience, in this data, is the strongest predictor of both urgency and pessimism. The consideration set still leans provider-native (OpenAI 34%, Google 30%, Anthropic 29%, Azure 25%), but the dedicated security vendors — Cloudflare, Cisco, Palo Alto, Okta, Check Point’s Lakera — draw early interest in the mid-to-high single digits, more than their current footprint. What the shopping does not yet include is the identity layer specifically. Twelve percent of the respondents include an agent-identity product — Okta for AI Agents, Microsoft Entra Agent ID, or a non-human identity platform — anywhere in their consideration set, and among the credential-sharing organizations that have already had an incident, identity consideration is essentially unchanged, at roughly one in ten. The control most directly implicated by the incident data is the one largely missing from the purchase plans. Whether this wave hardens the provider-native default or finally opens the door to purpose-built agent security — the identity and isolation controls the incidents call for — is the question this series will keep tracking. The bottom line: A security gap that autonomy will test first Organizations with more than 100 employees are giving AI agents real reach into systems and data while securing them with controls built for something else. More than half have already had an incident or near-miss; only a third give every agent its own scoped identity, and most still share credentials; only three in ten isolate their highest-risk agents; and the stack doing this work is overwhelmingly borrowed from the model providers and hyperscalers rather than purpose-built for agents. The uncomfortable pairing is confidence with exposure: satisfaction with the current tooling is among the highest in this series, yet spending is a thin slice of the security budget, only a third believe their defenses are ahead of AI-enabled attackers, and a clear majority are already planning to replace what they have. At 107 respondents in a single wave this is a directional read, skewed toward the mid-market — but the direction is clear: agent adoption is running ahead of agent security, and the controls that matter most when something fails — scoped identity and isolation — are the ones enterprises have built least. The agent security gap is not a coverage problem that a provider guardrail will close on its own; it is a problem of identity, isolation, and enforcement built for autonomous software. The open question for later waves is whether enterprises close it deliberately — or whether a confirmed incident closes it for them. Based on survey responses from 107 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. This is a directional read, not a precise measurement — the sample is self-selected and skews mid-market, so it's best read as the view from organizations actively standing up agent security rather than from the largest operators. Respondents are senior and buyer-credible (45% final decision-makers, 30% recommenders/influencers), spanning managers through the C-suite, and drawn primarily from Technology/Software, Manufacturing, Retail/E-commerce, and Healthcare/Life Sciences. Maestro News, gelişmeyi Maestro Dev ekseninde — yazılım, yapay zekâ, bulut ve ürün teslimatı — özgün Türkçe dilde yeniden çerçeveliyor.
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Railway, a San Francisco-based cloud platform that has quietly amassed two million developers without spending a dollar on marketing, announced Thursday that it raised $100 million in a Series B funding round, as surging demand for artificial intelligence applications exposes the limitations of legacy cloud infrastructure. TQ Ventures led the round, with participation from FPV Ventures, Redpoint, and Unusual Ventures. The investment values Railway as one of the most significant infrastructure startups to emerge during the AI boom, capitalizing on developer frustration with the complexity and cost of traditional platforms like Amazon Web Services and Google Cloud. "As AI models get better at writing code, more and more people are asking the age-old question: where, and how, do I run my applications?" said Jake Cooper, Railway's 28-year-old founder and chief executive, in an exclusive interview with VentureBeat. "The last generation of cloud primitives were slow and outdated, and now with AI moving everything faster, teams simply can't keep up." The funding is a dramatic acceleration for a company that has charted an unconventional path through the cloud computing industry. Railway raised just $24 million in total before this round, including a $20 million Series A from Redpoint in 2022. The company now processes more than 10 million deployments monthly and handles over one trillion requests through its edge network — metrics that rival far larger and better-funded competitors. Why three-minute deploy times have become unacceptable in the age of AI coding assistants Railway's pitch rests on a simple observation: the tools developers use to deploy and manage software were designed for a slower era. A standard build-and-deploy cycle using Terraform, the industry-standard infrastructure tool, takes two to three minutes. That delay, once tolerable, has become a critical bottleneck as AI coding assistants like Claude, ChatGPT, and Cursor can generate working code in seconds. "When godly intelligence is on tap and can solve any problem in three seconds, those amalgamations of systems become bottlenecks," Cooper told VentureBeat. "What was really cool for humans to deploy in 10 seconds or less is now table stakes for agents." The company claims its platform delivers deployments in under one second — fast enough to keep pace with AI-generated code. Customers report a tenfold increase in developer velocity and up to 65 percent cost savings compared to traditional cloud providers. These numbers come directly from enterprise clients, not internal benchmarks. Daniel Lobaton, chief technology officer at G2X, a platform serving 100,000 federal contractors, measured deployment speed improvements of seven times faster and an 87 percent cost reduction after migrating to Railway. His infrastructure bill dropped from $15,000 per month to approximately $1,000. "The work that used to take me a week on our previous infrastructure, I can do in Railway in like a day," Lobaton said. "If I want to spin up a new service and test different architectures, it would take so long on our old setup. In Railway I can launch six services in two minutes." Inside the controversial decision to abandon Google Cloud and build data centers from scratch What distinguishes Railway from competitors like Render and Fly.io is the depth of its vertical integration. In 2024, the company made the unusual decision to abandon Google Cloud entirely and build its own data centers, a move that echoes the famous Alan Kay maxim: "People who are really serious about software should make their own hardware." "We wanted to design hardware in a way where we could build a differentiated experience," Cooper said. "Having full control over the network, compute, and storage layers lets us do really fast build and deploy loops, the kind that allows us to move at 'agentic speed' while staying 100 percent the smoothest ride in town." The approach paid dividends during recent widespread outages that affected major cloud providers — Railway remained online throughout. This soup-to-nuts control enables pricing that undercuts the hyperscalers by roughly 50 percent and newer cloud startups by three to four times. Railway charges by the second for actual compute usage: $0.00000386 per gigabyte-second of memory, $0.00000772 per vCPU-second, and $0.00000006 per gigabyte-second of storage. There are no charges for idle virtual machines — a stark contrast to the traditional cloud model where customers pay for provisioned capacity whether they use it or not. "The conventional wisdom is that the big guys have economies of scale to offer better pricing," Cooper noted. "But when they're charging for VMs that usually sit idle in the cloud, and we've purpose-built everything to fit much more density on these machines, you have a big opportunity." How 30 employees built a platform generating tens of millions in annual revenue Railway has achieved its scale with a team of just 30 employees generating tens of millions in annual revenue — a ratio of revenue per employee that would be exceptional even for established software companies. The company grew revenue 3.5 times last year and continues to expand at 15 percent month-over-month. Cooper emphasized that the fundraise was strategic rather than necessary. "We're default alive; there's no reason for us to raise money," he said. "We raised because we see a massive opportunity to accelerate, not because we needed to survive." The company hired its first salesperson only last year and employs just two solutions engineers. Nearly all of Railway's two million users discovered the platform through word of mouth — developers telling other developers about a tool that actually works. "We basically did the standard engineering thing: if you build it, they will come," Cooper recalled. "And to some degree, they came." From side projects to Fortune 500 deployments: Railway's unlikely corporate expansion Despite its grassroots developer community, Railway has made significant inroads into large organizations. The company claims that 31 percent of Fortune 500 companies now use its platform, though deployments range from company-wide infrastructure to individual team projects. Notable customers include Bilt, the loyalty program company; Intuit's GoCo subsidiary; TripAdvisor's Cruise Critic; and MGM Resorts. Kernel, a Y Combinator-backed startup providing AI infrastructure to over 1,000 companies, runs its entire customer-facing system on Railway for $444 per month. "At my previous company Clever, which sold for $500 million, I had six full-time engineers just managing AWS," said Rafael Garcia, Kernel's chief technology officer. "Now I have six engineers total, and they all focus on product. Railway is exactly the tool I wish I had in 2012." For enterprise customers, Railway offers security certifications including SOC 2 Type 2 compliance and HIPAA readiness, with business associate agreements available upon request. The platform provides single sign-on authentication, comprehensive audit logs, and the option to deploy within a customer's existing cloud environment through a "bring your own cloud" configuration. Enterprise pricing starts at custom levels, with specific add-ons for extended log retention ($200 monthly), HIPAA BAAs ($1,000), enterprise support with SLOs ($2,000), and dedicated virtual machines ($10,000). The startup's bold strategy to take on Amazon, Google, and a new generation of cloud rivals Railway enters a crowded market that includes not only the hyperscale cloud providers—Amazon Web Services, Microsoft Azure, and Google Cloud Platform—but also a growing cohort of developer-focused platforms like Vercel, Render, Fly.io, and Heroku. Cooper argues that Railway's competitors fall into two camps, neither of which has fully committed to the new infrastructure model that AI demands. "The hyperscalers have two competing systems, and they haven't gone all-in on the new model because their legacy revenue stream is still printing money," he observed. "They have this mammoth pool of cash coming from people who provision a VM, use maybe 10 percent of it, and still pay for the whole thing. To what end are they actually interested in going all the way in on a new experience if they don't really need to?" Against startup competitors, Railway differentiates by covering the full infrastructure stack. "We're not just containers; we've got VM primitives, stateful storage, virtual private networking, automated load balancing," Cooper said. "And we wrap all of this in an absurdly easy-to-use UI, with agentic primitives so agents can move 1,000 times faster." The platform supports databases including PostgreSQL, MySQL, MongoDB, and Redis; provides up to 256 terabytes of persistent storage with over 100,000 input/output operations per second; and enables deployment to four global regions spanning the United States, Europe, and Southeast Asia. Enterprise customers can scale to 112 vCPUs and 2 terabytes of RAM per service. Why investors are betting that AI will create a thousand times more software than exists today Railway's fundraise reflects broader investor enthusiasm for companies positioned to benefit from the AI coding revolution. As tools like GitHub Copilot, Cursor, and Claude become standard fixtures in developer workflows, the volume of code being written — and the infrastructure needed to run it — is expanding dramatically. "The amount of software that's going to come online over the next five years is unfathomable compared to what existed before — we're talking a thousand times more software," Cooper predicted. "All of that has to run somewhere." The company has already integrated directly with AI systems, building what Cooper calls "loops where Claude can hook in, call deployments, and analyze infrastructure automatically." Railway released a Model Context Protocol server in August 2025 that allows AI coding agents to deploy applications and manage infrastructure directly from code editors. "The notion of a developer is melting before our eyes," Cooper said. "You don't have to be an engineer to engineer things anymore — you just need critical thinking and the ability to analyze things in a systems capacity." What Railway plans to do with $100 million and zero marketing experience Railway plans to use the new capital to expand its global data center footprint, grow its team beyond 30 employees, and build what Cooper described as a proper go-to-market operation for the first time in the company's five-year history. "One of my mentors said you raise money when you can change the trajectory of the business," Cooper explained. "We've built all the required substrate to scale indefinitely; what's been holding us back is simply talking about it. 2026 is the year we play on the world stage." The company's investor roster reads like a who's who of developer infrastructure. Angel investors include Tom Preston-Werner, co-founder of GitHub; Guillermo Rauch, chief executive of Vercel; Spencer Kimball, chief executive of Cockroach Labs; Olivier Pomel, chief executive of Datadog; and Jori Lallo, co-founder of Linear. The timing of Railway's expansion coincides with what many in Silicon Valley view as a fundamental shift in how software gets made. Coding assistants are no longer experimental curiosities — they have become essential tools that millions of developers rely on daily. Each line of AI-generated code needs somewhere to run, and the incumbents, by Cooper's telling, are too wedded to their existing business models to fully capitalize on the moment. Whether Railway can translate developer enthusiasm into sustained enterprise adoption remains an open question. The cloud infrastructure market is littered with promising startups that failed to break the grip of Amazon, Microsoft, and Google. But Cooper, who previously worked as a software engineer at Wolfram Alpha, Bloomberg, and Uber before founding Railway in 2020, seems unfazed by the scale of his ambition. "In five years, Railway [will be] the place where software gets created and evolved, period," he said. "Deploy instantly, scale infinitely, with zero friction. That's the prize worth playing for, and there's no bigger one on offer." For a company that built a $100 million business by doing the opposite of what conventional startup wisdom dictates — no marketing, no sales team, no venture hype—the real test begins now. Railway spent five years proving that developers would find a better mousetrap on their own. The next five will determine whether the rest of the world is ready to get on board. Maestro News, gelişmeyi Maestro Dev ekseninde — yazılım, yapay zekâ, bulut ve ürün teslimatı — özgün Türkçe dilde yeniden çerçeveliyor.
Bulut & SaaS
<div class="block-paragraph"><p data-block-key="2ncmz">Scientists today face challenges of extraordinary scale and complexity. From shaping and simulating the intricate dynamics of fusion plasma, to exploring the vast search space of new materials, to making sense of the exabytes of data pouring out of the world's most advanced experimental facilities. The demands on modern research are unprecedented. <a href="https://deepmind.google/research/projects/" target="_blank">Frontier AI</a> can help address these challenges, while accelerating groundbreaking scientific discoveries.</p><p data-block-key="dc2rb">In December, we shared our commitment to the White House's <a href="https://www.whitehouse.gov/presidential-actions/2025/11/launching-the-genesis-mission/" target="_blank">Genesis Mission</a> — the national effort to harness AI and double the pace of American scientific discovery within a decade. Since then, Google DeepMind (GDM) <a href="https://deepmind.google/blog/google-deepmind-supports-us-department-of-energy-on-genesis/" target="_blank">announced an early access program</a> that provides AI for science tools to all 17 Department of Energy (DOE) National Laboratories, and Google Public Sector <a href="https://cloud.google.com/blog/topics/public-sector/how-google-public-sector-and-google-deepmind-can-power-the-genesis-mission-and-a-new-era-of-scientific-discovery">shared how Gemini for Government</a> could serve as an AI backbone for the DOE.</p><p data-block-key="6uqnd">Today, at the DOE Genesis Mission Summit 2026, we are expanding this by committing $40 million of AI tokens and cloud credits for researchers in support of the Genesis Mission.</p><h3 data-block-key="bhcl0"><b>Frontier AI tools for scientific discovery</b></h3><p data-block-key="f2lp3">Under this expanded commitment, we will first provide DOE’s Genesis Mission awardees in-kind access to GDM’s frontier AI for science portfolio, including:</p><ul><li data-block-key="35aj5"><a href="https://cloud.google.com/blog/products/ai-machine-learning/alphaevolve-is-available-for-everyone"><b>AlphaEvolve</b></a> — a Gemini-powered coding and discovery agent, for designing advanced algorithms.</li><li data-block-key="dm5md"><a href="https://blog.google/innovation-and-ai/products/google-deepmind-isomorphic-alphafold-3-ai-model/" target="_blank"><b>AlphaFold 3</b></a> — a model for predicting the structure and interactions of proteins and other biomolecules.</li><li data-block-key="chpg8"><a href="https://deepmind.google/blog/alphagenome-ai-for-better-understanding-the-genome/" target="_blank"><b>AlphaGenome</b></a> — a tool for understanding how variation in DNA, including the non-coding genome, shapes biology and disease.</li><li data-block-key="294jv"><a href="https://deepmind.google/science/weathernext/" target="_blank"><b>WeatherNext</b></a> — a state-of-the-art family of AI weather forecasting models for mapping weather conditions.</li><li data-block-key="3s8k3"><a href="https://deepmind.google/blog/alphaearth-foundations-helps-map-our-planet-in-unprecedented-detail/" target="_blank"><b>AlphaEarth Foundations</b></a> — a foundational AI model for mapping and understanding our planet in unprecedented detail.</li></ul><p data-block-key="7nar0">Second, we will provide Gemini for Government seats and tokens for one year to tens of thousands of users across the DOE National Laboratories’ operations, research, and management teams. This secure platform supports the full breadth of work from the research bench to the administration of specialized user facilities serving the entire scientific community, providing a single secure foundation that the DOE mission can depend on.</p><h3 data-block-key="8bgto"><b>AI for science tools in action across the laboratory ecosystem</b></h3><p data-block-key="7s9eq">While we have a lot of work still to do, the practical impact of the Genesis Mission is already coming to life across the laboratory ecosystem.</p><p data-block-key="f6839">At <a href="https://www.pnnl.gov/" target="_blank">Pacific Northwest National Laboratory (PNNL)</a>, senior scientist Dr. Henry Kvinge is using AlphaEvolve to map out massive mathematical systems that are far too complex for humans to explore by hand. The AI uncovers hidden connections automatically, fast-tracking discoveries that would normally take researchers years to find.</p><p data-block-key="9v4f3">“Modern math relies on abstraction, but combinatorics offers concrete models that make complex geometry and algebra easier to grasp. We’ve found that systems like AlphaEvolve are perfect for this search,” said Dr. Kvinge. “By leveraging the broad mathematical knowledge of LLMs, we can automate the exploration of countless angles. We’re still experimenting, but the discoveries are already shaping our future research.”</p><p data-block-key="1hkb1">At the <a href="https://www.nlr.gov/" target="_blank">National Laboratory of the Rockies (NLR)</a>, researchers are utilizing Gemini to fundamentally change how they interact with physical laboratory hardware. Dr. Steven R. Spurgeon, a senior materials data scientist at NLR, leads a pioneering program in autonomous materials discovery.</p><p data-block-key="cklfi">"Our collaboration has allowed us to build an autonomous experimentation capability," said Dr. Spurgeon. "By deploying Gemini in our instruments, we cut microscope calibration time from over 90 minutes to about 13 minutes (eight times faster) and reduced the manual steps needed to focus an image from as many as 50 down to two. That's time and attention we've given back to the science itself, enabling genuinely autonomous workflows that observe, reason, and decide in real time. This has helped us explore parts of the material design space we simply could not have reached through manual operation alone."</p><h3 data-block-key="c0sn0"><b>Driving American innovation</b></h3><p data-block-key="eee4l">The Genesis Mission represents an opportunity to transform research and science across America. By providing access to advanced AI tools, we aim to help scientists accelerate breakthroughs across critical energy, security, and scientific challenges. To learn more about how these AI capabilities can support your research initiatives, join us at the upcoming <a href="https://events.govexec.com/google-public-sector-summit/" target="_blank">Google Public Sector Summit</a> in October.</p></div> Maestro News, gelişmeyi Maestro Dev ekseninde — yazılım, yapay zekâ, bulut ve ürün teslimatı — özgün dilde yeniden çerçeveliyor.
Bilişim Sektörü
Cascade raises $3.5M to help construction firms find and win projects Maestro News, gelişmeyi Maestro Dev ekseninde — yazılım, yapay zekâ, bulut ve ürün teslimatı — özgün dilde yeniden çerçeveliyor.
Bilişim Sektörü
The browser wars aren’t about search anymore — here are the best alternatives to Chrome and Safari Maestro News, gelişmeyi Maestro Dev ekseninde — yazılım, yapay zekâ, bulut ve ürün teslimatı — özgün dilde yeniden çerçeveliyor.
Bilişim Sektörü
Passionfroot raises $15M to expand its B2B creator marketplace to the US Maestro News, gelişmeyi Maestro Dev ekseninde — yazılım, yapay zekâ, bulut ve ürün teslimatı — özgün dilde yeniden çerçeveliyor.
Bilişim Sektörü
WhatsApp adds Apple CarPlay and Android Auto upgrades, iPad sign-ups, and more Maestro News, gelişmeyi Maestro Dev ekseninde — yazılım, yapay zekâ, bulut ve ürün teslimatı — özgün dilde yeniden çerçeveliyor.
Yazılım
<img src="https://res.infoq.com/news/2026/07/anthropic-claude-containment/en/headerimage/Anthropic-Header-1784707138026.jpeg"/><p>Anthropic detailed the containment architectures it uses for Claude across its products. It argues that agent safety depends on placing deterministic limits on an agent’s filesystem, network, and execution environment rather than on permission prompts or safeguards. Most notably, it examines failures at trust boundaries and along permitted egress paths that led Anthropic to revise those designs.</p> <i>By Eran Stiller</i> Maestro News, gelişmeyi Maestro Dev ekseninde — yazılım, yapay zekâ, bulut ve ürün teslimatı — özgün dilde yeniden çerçeveliyor.
Yazılım
<img src="https://res.infoq.com/presentations/agent-software-engineering/en/mediumimage/jake-medium-1784183841742.jpeg"/><p>Jake Mannix discusses moving AI agents past chaotic "1970s BASIC" architectures. He shares how implementing an intermediate protocol layer allows engineering leaders to build versioned, encapsulated "virtual tools." This design enables interface mapping, dynamic schema projection, and runtime taint tracking to proactively eliminate data exfiltration risks without slowing velocity.</p> <i>By Jake Mannix</i> Maestro News, gelişmeyi Maestro Dev ekseninde — yazılım, yapay zekâ, bulut ve ürün teslimatı — özgün dilde yeniden çerçeveliyor.
Yazılım
<img src="https://res.infoq.com/news/2026/07/aws-billing-estimates-incident/en/headerimage/generatedHeaderImage-1784456911552.jpg"/><p>A configuration change in AWS's bill computation system showed customers estimated bills in the billions and trillions of dollars for over 24 hours. AWS's own alarms detected the anomalies but failed to halt bill generation or page engineers; customer escalations alerted the company 4.5 hours later. Budget and cost anomaly alerts were disabled platform-wide during mitigation.</p> <i>By Steef-Jan Wiggers</i> Maestro News, gelişmeyi Maestro Dev ekseninde — yazılım, yapay zekâ, bulut ve ürün teslimatı — özgün dilde yeniden çerçeveliyor.
Yazılım
<img src="https://res.infoq.com/news/2026/07/google-gke-ai-security-blueprint/en/headerimage/generatedHeaderImage-1784570060139.jpg"/><p>Google Cloud has published a new blueprint setting out how organisations should secure artificial intelligence workloads running on Google Kubernetes Engine, arguing that the shift from prototype to production has outpaced traditional security models. The document sets out a three layer approach covering infrastructure, model integrity and application security.</p> <i>By Matt Saunders</i> Maestro News, gelişmeyi Maestro Dev ekseninde — yazılım, yapay zekâ, bulut ve ürün teslimatı — özgün dilde yeniden çerçeveliyor.
Bulut & SaaS
<div class="block-paragraph_advanced"><p><span style="vertical-align: baseline;">We are living in the golden age of the weekend AI side project. Thanks to agentic engineering and LLMs, the time to go from a blank IDE to a functional local application has dropped from quarters to hours. You can build your wildest ideas over a cup of coffee.</span></p> <p><span style="vertical-align: baseline;">But inside an enterprise ecosystem with rigid infrastructure and millions of users, vibe coding hits an invisible wall. Your local prototype falls apart against corporate networks, cascading errors, or getting blocked by leadership terrified of operational volatility.</span></p> <p><span style="vertical-align: baseline;">The </span><a href="https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf" rel="noopener" target="_blank"><span style="text-decoration: underline; vertical-align: baseline;">data</span></a><span style="vertical-align: baseline;"> is sobering: only 5% of AI prototypes make it to production; the other 95% fall into the validation abyss.</span></p> <p><span style="vertical-align: baseline;">For developers, watching people on social media ship lightning-fast AI deployments while you’re stuck in endless validation loops is maddening. To figure out how to bridge this chasm, I went into the engineering trenches at YouTube to see how they manage this exact speed-versus-risk paradox. What I discovered completely rewrites the playbook on AI software development lifecycle (SDLC) design.</span></p> <h3><span style="vertical-align: baseline;">The risk-vs-speed paradox</span></h3> <p><span style="vertical-align: baseline;">When you are solo-building, failure is cheap. Writing agentic code is like piloting a nimble jet fighter—if an AI agent misbehaves, you rewrite the prompt and instantly restart the server.</span></p> <p><span style="vertical-align: baseline;">But as AI engineering leader </span><a href="https://addyosmani.com/" rel="noopener" target="_blank"><span style="text-decoration: underline; vertical-align: baseline;">Addy Osmani</span></a><span style="vertical-align: baseline;"> points out in our premiere of </span><a href="http://goo.gle/emergent" rel="noopener" target="_blank"><span style="font-style: italic; text-decoration: underline; vertical-align: baseline;">Emergent</span></a><span style="vertical-align: baseline;">, unconstrained agentic orchestration inside an enterprise introduces an unpredictable blast radius. Addy recalls running ten parallel agents on a personal project, context-hopping and pushing code based purely on quick previews. The technical debt accumulated fast, breaking two apps catastrophically because the modifications weren't properly isolated.</span></p> <p><span style="vertical-align: baseline;">Amplify that risk to the scale of </span><strong style="vertical-align: baseline;">YouTube</strong><span style="vertical-align: baseline;">. Its infrastructure handles billions of users on a robust, 20-year-old codebase. It is essentially a public utility; you cannot risk overloading it with experimental technical debt. Protecting a platform of this scale requires extensive, slow guardrails:</span></p></div> <div class="block-image_full_width"> <div class="article-module h-c-page"> <div class="h-c-grid"> <figure class="article-image--large h-c-grid__col h-c-grid__col--6 h-c-grid__col--offset-3 " > <img src="https://storage.googleapis.com/gweb-cloudblog-publish/images/1_Gemini_Generated_Image.max-1000x1000.jpg" alt="1_Gemini_Generated_Image"> </a> </figure> </div> </div> </div> <div class="block-paragraph_advanced"><p><span style="vertical-align: baseline;">By the time you build a primitive demo through this pipeline, the underlying AI models have evolved, leaving your idea out of date. </span><strong style="vertical-align: baseline;">How do you move at lightspeed while minimizing systemic risk? </strong></p> <h3><span style="vertical-align: baseline;">YouTube’s AI prototyping stack</span></h3> <p><span style="vertical-align: baseline;">Deepmind and former YouTube software engineer, </span><a href="https://www.linkedin.com/in/benji-bear-25972313a/" rel="noopener" target="_blank"><span style="text-decoration: underline; vertical-align: baseline;">Benji Bear</span></a><span style="vertical-align: baseline;">, solved this puzzle not by accelerating reviews, but by changing infrastructure philosophy. He and his team built a </span><strong style="vertical-align: baseline;">prototyping stack </strong><span style="vertical-align: baseline;">— a unified design-to-code lifecycle platform that completely decouples rapid experimentation from mainline production servers. It systematically solves the two primary friction points of developer velocity.</span></p> <h3><strong style="vertical-align: baseline;">Decoupling the data layer</strong></h3> <p><span style="vertical-align: baseline;">Isolating a standalone app completely causes a "blank canvas" problem where you can't test prototypes against realistic conditions. To solve this, developers bootstrap their ideas using pre-built </span><a href="https://aistudio.google.com/" rel="noopener" target="_blank"><span style="text-decoration: underline; vertical-align: baseline;">Google AI Studio</span></a><span style="vertical-align: baseline;"> templates. These templates hook into a proxy server set up on Google Cloud for prototype-approved read-only data. This instantly grants the prototype pre-authenticated, read-only API access to live metadata bundles (playlists, videos, channels) via strict tokens.</span></p></div> <div class="block-image_full_width"> <div class="article-module h-c-page"> <div class="h-c-grid"> <figure class="article-image--large h-c-grid__col h-c-grid__col--6 h-c-grid__col--offset-3 " > <img src="https://storage.googleapis.com/gweb-cloudblog-publish/images/2_Gemini_Generated_Image.max-1000x1000.jpg" alt="2_Gemini_Generated_Image"> </a> </figure> </div> </div> </div> <div class="block-paragraph_advanced"><p><span style="vertical-align: baseline;">Developers get the technical accuracy of live production parameters without any ability to write back to, pollute, or crash core databases. </span></p> <h3><span style="vertical-align: baseline;">Live UI injection</span></h3> <p><span style="vertical-align: baseline;">When a concept requires true real-world validation, the stack offers client-side </span><strong style="vertical-align: baseline;">YouTube Extension wrappers</strong><span style="vertical-align: baseline;">. This wrapper acts as glue code, allowing developers to inject their experimental features directly into the actual, live production web surface of YouTube.</span><span style="vertical-align: baseline;"> </span><span style="vertical-align: baseline;">Code-split chunk safeguards isolate this from production binaries, allowing prototype updates to deploy to a safe staging environment in minutes. </span></p> <p><span style="vertical-align: baseline;">The result? YouTube went from taking multiple quarters to vet an idea to launching several successful prototypes — including </span><span style="font-style: italic; vertical-align: baseline;">YouTube Recap</span><span style="vertical-align: baseline;"> and </span><span style="font-style: italic; vertical-align: baseline;">Ask YouTube </span><span style="vertical-align: baseline;">— straight to user research studies (UXR) in weeks.</span></p> <h3><span style="vertical-align: baseline;">Embrace throw-away code</span></h3> <p><span style="vertical-align: baseline;">Implementing this stack requires a profound psychological shift. Engineers are trained to treat code as permanent infrastructure, polishing and refactoring it until it’s pristine. But Benji’s core enterprise AI philosophy here is simple: </span><strong style="vertical-align: baseline;">Embrace throw-away code.</strong></p> <p><span style="vertical-align: baseline;">Google AI Studio prototypes are meant to be messy with some technical debt; their objective is to validate product-market fit using quantitative data. Trying to refactor a chaotic, AI-generated app into an enterprise codebase is an architectural trap that can create friction.</span></p></div> <div class="block-image_full_width"> <div class="article-module h-c-page"> <div class="h-c-grid"> <figure class="article-image--large h-c-grid__col h-c-grid__col--6 h-c-grid__col--offset-3 " > <img src="https://storage.googleapis.com/gweb-cloudblog-publish/images/3_Gemini_Generated_Image.max-1000x1000.jpg" alt="3_Gemini_Generated_Image"> </a> </figure> </div> </div> </div> <div class="block-paragraph_advanced"><p><span style="vertical-align: baseline;">But because Google AI Studio builds your prototype directly onto a mirrored version of production infrastructure, you establish a highly accurate baseline from day one. You still discard the messy, AI-generated script, but when an idea proves successful, rewriting it for production becomes significantly faster, cheaper, and safely positioned later in the development lifecycle—giving you a verified blueprint to code against rather than a blank canvas. </span></p> <h3><span style="vertical-align: baseline;">Move fast without breaking things</span></h3> <p><span style="vertical-align: baseline;">The core realization here is that a 95% failure rate isn’t a bug — it is the strategy. We should design environments that encourage our teams to fail more frequently and safely.</span></p> <p><span style="vertical-align: baseline;">AI has plummeted the cost of code generation. Consequently, our roles are shifting from syntax gatekeepers to </span><strong style="vertical-align: baseline;">system architects</strong><span style="vertical-align: baseline;">. Our job is to design the bridges, read-only sandboxes, and isolated pipelines that empower teams to test wild ideas without triggering catastrophic meltdowns.</span></p> <p><span style="vertical-align: baseline;">The biggest risk isn't breaking a server with messy AI code; it's missing the technological moment because validation loops are too slow. By building structural constraints that make failure safe, you give your team the freedom to run at hyper-speed.</span></p> <p><span style="font-style: italic; vertical-align: baseline;">To see the full technical breakdown, interview clips with YouTube's core infrastructure engineers, and a look inside the Google AI Studio Proto-Stack, watch our premiere episode of </span><a href="http://goo.gle/emergent" rel="noopener" target="_blank"><strong style="font-style: italic; text-decoration: underline; vertical-align: baseline;">Emergent</strong></a><span style="font-style: italic; vertical-align: baseline;"> on YouTube.</span></p></div> Maestro News, gelişmeyi Maestro Dev ekseninde — yazılım, yapay zekâ, bulut ve ürün teslimatı — özgün dilde yeniden çerçeveliyor.
Bulut & SaaS
<div class="block-paragraph_advanced"><p><a href="https://cloud.google.com/alloydb"><span style="text-decoration: underline; vertical-align: baseline;">AlloyDB</span></a><span style="vertical-align: baseline;"> is a fully managed, PostgreSQL-compatible database service built for your most demanding enterprise workloads. It combines the best of open source PostgreSQL with Google’s advanced technology, offering massive scalability, high availability, and native AI capabilities. It serves as a performant relational store, a unified backend for vector and full text search, and an analytics engine that is up to 100x faster than standard PostgreSQL. </span></p> <p><span style="vertical-align: baseline;">Vector search is the foundation of modern AI and Retrieval Augmented Generation (RAG) applications. For developers using AlloyDB and other PostgreSQL databases, </span><a href="https://github.com/pgvector/pgvector" rel="noopener" target="_blank"><code style="text-decoration: underline; vertical-align: baseline;">pgvector</code></a><span style="vertical-align: baseline;"> is a widely adopted extension for storing, indexing, and querying vector embeddings, and HNSW (Hierarchical Navigable Small World) is a highly efficient graph-based algorithm designed for approximate nearest neighbor search across multi-layered structures. With </span><a href="https://docs.cloud.google.com/alloydb/docs/ai/accelerate-with-ce"><span style="text-decoration: underline; vertical-align: baseline;">columnar engine accelerated HNSW</span></a><span style="vertical-align: baseline;"> in AlloyDB (now in preview), you can achieve up to 4x higher queries per second (QPS) for vector search compared to standard PostgreSQL HNSW.</span></p> <p><span style="vertical-align: baseline;">Enterprise AI applications face a constant trade-off between speed and accuracy. When searching through millions or billions of vectors, maximizing Queries per Second (QPS) without sacrificing search quality (recall) is critical for scaling production workloads. The PostgreSQL </span><code style="vertical-align: baseline;">pgvector</code><span style="vertical-align: baseline;"> extension offers HNSW as one of the indexes that can speed up Approximate Nearest Neighbor (ANN) searches. Let’s dive deep into how AlloyDB solves the speed vs. accuracy trade-off. </span></p> <p><span style="vertical-align: baseline;">Note: While this post focuses on HNSW performance, it’s worth noting that HNSW is just one part of AlloyDB’s advanced vector toolkit. AlloyDB also features </span><a href="https://docs.cloud.google.com/alloydb/docs/ai/create-scann-index"><span style="text-decoration: underline; vertical-align: baseline;">ScaNN</span></a><span style="vertical-align: baseline;">—a cutting-edge index backed by over 14 years of Google Research—giving you the flexibility to choose the perfect index for your workload. Additionally, for use cases demanding absolute precision, standard k-nearest neighbor (KNN) search is always available for 100% recall. Check out our </span><a href="https://docs.cloud.google.com/alloydb/docs/ai/choose-index-strategy"><span style="text-decoration: underline; vertical-align: baseline;">Choose a Vector Index Guide</span></a><span style="vertical-align: baseline;"> to see how they stack up.</span></p></div> <div class="block-aside"><dl> <dt>aside_block</dt> <dd>&lt;ListValue: [StructValue([(&#x27;title&#x27;, &#x27;Get started with a 30-day AlloyDB free trial instance&#x27;), (&#x27;body&#x27;, &lt;wagtail.rich_text.RichText object at 0x7f019660e400&gt;), (&#x27;btn_text&#x27;, &#x27;Start building for free&#x27;), (&#x27;href&#x27;, &#x27;http://goo.gle/try_alloydb&#x27;), (&#x27;image&#x27;, None)])]&gt;</dd> </dl></div> <div class="block-paragraph_advanced"><h3><strong style="vertical-align: baseline;">First, what is the AlloyDB columnar engine? </strong></h3> <p><span style="vertical-align: baseline;">The </span><a href="https://docs.cloud.google.com/alloydb/docs/columnar-engine/about"><span style="text-decoration: underline; vertical-align: baseline;">AlloyDB columnar engine</span></a><span style="vertical-align: baseline;"> is a built-in, in-memory cache that automatically stores frequently queried data in a specialized, scan-optimized columnar format. It allows AlloyDB to handle heavy analytical queries up to 100x faster than standard PostgreSQL. Additionally, it accelerates ANN searches by storing the index in memory, using a vectorized memory layout for fast traversals, and bypassing standard PostgreSQL buffer manager overhead. </span></p> <h3><strong style="vertical-align: baseline;">Performance visualization</strong></h3> <p><span style="vertical-align: baseline;">To understand the real-world performance characteristics of columnar engine Accelerated HNSW, we plotted standard QPS vs Recall curves for the GloVe 100 Angular dataset by searching more than 1M records with a limit of 100.</span></p> <p><span style="vertical-align: baseline;">Running this </span><a href="https://colab.research.google.com/github/GoogleCloudPlatform/python-docs-samples/blob/main/alloydb/notebooks/columnar_engine_accelerated_hnsw_vector_search_benchmark.ipynb" rel="noopener" target="_blank"><span style="text-decoration: underline; vertical-align: baseline;">benchmark script</span></a><span style="vertical-align: baseline;"> yields the following visualization:</span></p></div> <div class="block-image_full_width"> <div class="article-module h-c-page"> <div class="h-c-grid"> <figure class="article-image--large h-c-grid__col h-c-grid__col--6 h-c-grid__col--offset-3 " > <img src="https://storage.googleapis.com/gweb-cloudblog-publish/images/1_r57mjyN.max-1000x1000.png" alt="1"> </a> <figcaption class="article-image__caption "><p data-block-key="xai0q">Note: These measurements were taken on an AlloyDB C4A 16vCPU machine. Due to the inherent randomness in HNSW graph building, results may slightly vary across runs.</p></figcaption> </figure> </div> </div> </div> <div class="block-paragraph_advanced"><p><span style="vertical-align: baseline;">The data reveals two transformative benefits:</span></p> <ol> <li role="presentation"><strong style="vertical-align: baseline;">Massive performance throughput gains: </strong><span style="vertical-align: baseline;">For any given target recall (e.g. 0.95), QPS is increased by approximately </span><strong style="vertical-align: baseline;">4.2x to 4.9x</strong><span style="vertical-align: baseline;">. This allows you to handle significantly more concurrent vector searches on the same hardware.</span></li> <li role="presentation"><strong style="vertical-align: baseline;">Significant recall (accuracy) improvement: </strong><span style="vertical-align: baseline;">Conversely, at a fixed QPS level, columnar engine accelerated HNSW provides a substantial boost in recall. For example, we saw that at ~350 QPS (in the above chart), enabling the columnar engine improves recall from roughly </span><strong style="vertical-align: baseline;">0.78 to over 0.94 </strong><span style="vertical-align: baseline;">– a </span><strong style="vertical-align: baseline;">0.163 recall gain</strong><span style="vertical-align: baseline;">. This means your AI applications get much more accurate results without any latency impact.</span></li> </ol> <p><span style="vertical-align: baseline;">It is important to note that the baseline (blue line) already represents the index being fully cached in the PostgreSQL shared buffer cache. The performance gains shown here are not the result of moving data from disk to RAM, but rather the result of a more efficient memory architecture.</span></p> <h3><strong style="vertical-align: baseline;">How it works: Columnar engine Accelerated HNSW</strong></h3> <p><span style="vertical-align: baseline;">In standard PostgreSQL architectures, index operations utilize the shared buffer cache. Even when data is fully in-memory, the database still incurs significant overhead from the buffer manager, which must handle operations such as page pinning and unpinning, lock acquisition, buffer table lookups, and Least Recently Used (LRU) management.</span></p> <p><span style="vertical-align: baseline;">AlloyDB's </span><strong style="vertical-align: baseline;">columnar engine </strong><span style="vertical-align: baseline;">is a built-in, in-memory cache that stores data in a specialized, scan-optimized format.</span></p> <p><span style="vertical-align: baseline;">With this release, AlloyDB can use </span><strong style="vertical-align: baseline;">columnar engine accelerated HNSW </strong><span style="vertical-align: baseline;">to:</span></p> <ul> <li role="presentation"><strong style="vertical-align: baseline;">Pin the index: </strong><span style="vertical-align: baseline;">The </span><code style="vertical-align: baseline;">pgvector</code><span style="vertical-align: baseline;"> HNSW index is pinned (kept persistently in-memory to ensure fast access) directly into the columnar engine’s memory.</span></li> <li role="presentation"><strong style="vertical-align: baseline;">Vectorized access: </strong><span style="vertical-align: baseline;">It utilizes a memory layout specifically designed for the high-concurrency, pointer-heavy traversals required by HNSW graphs.</span></li> <li role="presentation"><strong style="vertical-align: baseline;">Bypass buffer overhead: </strong><span style="vertical-align: baseline;">By navigating the graph in a specialized memory space, AlloyDB avoids the standard buffer manager bottlenecks. This architectural shift is what enables the dramatic QPS and recall improvements shown above, even when comparing against a fully-cached standard index.</span></li> </ul> <h3><strong style="vertical-align: baseline;">Why it Matters</strong></h3> <p><span style="vertical-align: baseline;">For enterprise-scale applications, this isn't just about a faster database—it's about cost and quality:</span></p> <ul> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> <p role="presentation"><strong style="vertical-align: baseline;">Reduced infrastructure costs:</strong><span style="vertical-align: baseline;"> Achieve the same performance with significantly lower compute resources.</span></p> </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> <p role="presentation"><strong style="vertical-align: baseline;">Better AI accuracy:</strong><span style="vertical-align: baseline;"> Reach higher recall and quality at speeds that were previously only possible for "draft" (high-speed, lower-accuracy results) quality search.</span></p> </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> <p role="presentation"><strong style="vertical-align: baseline;">No application changes required:</strong><span style="vertical-align: baseline;"> Because this is built into AlloyDB, you get these gains using the same standard </span><code style="vertical-align: baseline;">pgvector</code><span style="vertical-align: baseline;"> SQL syntax.</span></p> </li> </ul> <p><span style="vertical-align: baseline;">Note that the columnar engine does utilize memory, but it is highly compressed and meticulously managed. Because the engine stores vector data in an efficient columnar format, the memory footprint is minimal compared to the massive performance gains—making it a highly favorable trade-off for enterprise workloads.</span></p> <h3><strong style="vertical-align: baseline;">Quick Start Guide</strong></h3> <p><span style="vertical-align: baseline;">To try out </span><a href="https://docs.cloud.google.com/alloydb/docs/ai/accelerate-with-ce"><strong style="text-decoration: underline; vertical-align: baseline;">columnar engine accelerated HNSW</strong></a><span style="vertical-align: baseline;"> in AlloyDB, follow these steps:</span></p> <p><span style="vertical-align: baseline;">1. </span><strong><a href="https://docs.cloud.google.com/alloydb/docs/ai/accelerate-with-ce"><span style="text-decoration: underline; vertical-align: baseline;">Enable the columnar engine</span></a><span style="vertical-align: baseline;"> and index caching</span></strong></p> <p><span style="vertical-align: baseline;">Ensure that both </span><code style="vertical-align: baseline;">google_columnar_engine.enabled</code><span style="vertical-align: baseline;"> and </span><code style="vertical-align: baseline;">google_columnar_engine.enable_index_caching</code><span style="vertical-align: baseline;"> flags are set to </span><code style="vertical-align: baseline;">on</code><span style="vertical-align: baseline;"> for your AlloyDB instance.</span></p> <p><span style="vertical-align: baseline;">2. <strong>Add the HNSW Index to columnar engine</strong></span></p> <p><span style="vertical-align: baseline;">Once your HNSW index is created via </span><code style="vertical-align: baseline;">pgvector</code><span style="vertical-align: baseline;">, execute the following SQL command to cache it in the columnar engine:</span></p></div> <div class="block-code"><dl> <dt>code_block</dt> <dd>&lt;ListValue: [StructValue([(&#x27;code&#x27;, &quot;SELECT google_columnar_engine_add_index(&#x27;&lt;hnsw_index_name&gt;&#x27;);&quot;), (&#x27;language&#x27;, &#x27;&#x27;), (&#x27;caption&#x27;, &lt;wagtail.rich_text.RichText object at 0x7f0196625d90&gt;)])]&gt;</dd> </dl></div> <div class="block-paragraph_advanced"><p><span style="vertical-align: baseline;">3. <strong>Additional Resources</strong></span></p> <ul> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> <p role="presentation"><span style="vertical-align: baseline;">New to AlloyDB? Discover AlloyDB with a </span><a href="https://docs.cloud.google.com/alloydb/docs/free-trial-cluster"><span style="text-decoration: underline; vertical-align: baseline;">30-day free trial</span></a><span style="vertical-align: baseline;">.</span></p> </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> <p role="presentation"><a href="https://colab.research.google.com/github/GoogleCloudPlatform/python-docs-samples/blob/main/alloydb/notebooks/columnar_engine_accelerated_hnsw_vector_search_benchmark.ipynb" rel="noopener" target="_blank"><span style="text-decoration: underline; vertical-align: baseline;">Google Colab Notebook</span></a><span style="vertical-align: baseline;">: An end-to-end Python script to ingest the GloVe dataset, create indexes, and plot Recall vs QPS curves.</span></p> </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> <p role="presentation"><span style="vertical-align: baseline;">Is HNSW the right vector index choice for your use case? Check our ‘</span><a href="https://docs.cloud.google.com/alloydb/docs/ai/choose-index-strategy"><span style="text-decoration: underline; vertical-align: baseline;">Choose a vector index in AlloyDB AI</span></a><span style="vertical-align: baseline;">’ guide.</span></p> </li> </ul></div> Maestro News, gelişmeyi Maestro Dev ekseninde — yazılım, yapay zekâ, bulut ve ürün teslimatı — özgün dilde yeniden çerçeveliyor.
Bulut & SaaS
<div class="block-paragraph_advanced"><p><span style="vertical-align: baseline;">As adversarial AI threats accelerate attacks on code, security teams must counter them with machine-speed defenses that can automate code remediation and fight AI with AI.</span></p> <p><a href="https://cloud.google.com/security/codemender">CodeMender</a> is our managed code security agent, and starting today, we're bringing its code scanning and remediation capabilities directly to you in preview.</p> <p><span style="vertical-align: baseline;">CodeMender offers access to our generally available models via </span><a href="https://docs.cloud.google.com/gemini-enterprise-agent-platform/codemender"><span style="text-decoration: underline; vertical-align: baseline;">Gemini Enterprise Agent Platform</span></a><span style="vertical-align: baseline;">, or it can be deployed as a core component of </span><a href="https://cloud.google.com/security/ai-threat-defense"><span style="text-decoration: underline; vertical-align: baseline;">AI Threat Defense</span></a><span style="vertical-align: baseline;">. </span></p> <p><span style="vertical-align: baseline;">CodeMender also aligns with our </span><a href="https://cloud.google.com/blog/products/identity-security/cloud-ciso-perspectives-next-26-why-we-re-multicloud-and-multi-ai"><span style="text-decoration: underline; vertical-align: baseline;">multi-model approach</span></a><span style="vertical-align: baseline;">, so you can choose the right model to optimize for cost, speed, and deep scanning performance. It will support third-party frontier model options later this year.</span></p></div> <div class="block-video"> <div class="article-module article-video "> <figure> <a class="h-c-video h-c-video--marquee" href="https://youtube.com/watch?v=4DJD3RHOnPA" data-glue-modal-trigger="uni-modal-4DJD3RHOnPA-" data-glue-modal-disabled-on-mobile="true"> <div class="article-video__aspect-image" style="background-image: url(https://storage.googleapis.com/gweb-cloudblog-publish/images/1_sg64BeM.max-1000x1000.png);"> <span class="h-u-visually-hidden">How to find and fix code vulnerabilities autonomously with Google CodeMender.</span> </div> <svg role="img" class="h-c-video__play h-c-icon h-c-icon--color-white"> <use xlink:href="#mi-youtube-icon"></use> </svg> </a> <figcaption class="article-video__caption h-c-page"> <h4 class="h-c-headline h-c-headline--four h-u-font-weight-medium h-u-mt-std">Watch this overview of CodeMender in Gemini Enterprise Agent Platform.</h4> </figcaption> </figure> </div> <div class="h-c-modal--video" data-glue-modal="uni-modal-4DJD3RHOnPA-" data-glue-modal-close-label="Close Dialog"> <a class="glue-yt-video" data-glue-yt-video-autoplay="true" data-glue-yt-video-height="99%" data-glue-yt-video-vid="4DJD3RHOnPA" data-glue-yt-video-width="100%" href="https://youtube.com/watch?v=4DJD3RHOnPA" ng-cloak> </a> </div> </div> <div class="block-paragraph_advanced"><p><span style="vertical-align: baseline;">CodeMender can help you advance from passive scanning to automated code remediation, and reduce zero-day risk. It examines and remediates existing code security issues without sacrificing development velocity by:</span></p> <ul> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> <p role="presentation"><strong style="vertical-align: baseline;">Deploying the best-fit model</strong><span style="vertical-align: baseline;">. You can choose from multiple models to optimize for costs, speed, deep scanning, and coding performance.</span></p> </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> <p role="presentation"><strong style="vertical-align: baseline;">Automating machine-scale remediation</strong><span style="vertical-align: baseline;">. You can now eliminate remediation bottlenecks caused by manual verification and patching, while keeping developers in the loop.</span></p> </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> <p role="presentation"><strong style="vertical-align: baseline;">Prioritizing fixes by exploitability</strong><span style="vertical-align: baseline;">. You can run proof-of-concept exploits and execute simulations to verify that vulnerabilities in the code are exploitable, and prioritize resources on fixing the most critical issues first.</span></p> </li> </ul> <h3><strong style="vertical-align: baseline;">Find and fix vulnerabilities with AI</strong></h3> <p><span style="vertical-align: baseline;">Born from </span><a href="https://deepmind.google/blog/introducing-gemini-3-5-flash-cyber" rel="noopener" target="_blank"><span style="text-decoration: underline; vertical-align: baseline;">Google DeepMind's pioneering AI research</span></a><span style="vertical-align: baseline;">, CodeMender transforms vulnerability management from a manual bottleneck into an autonomous, high-speed system. Your developers and security practitioners can automatically scan software for flaws, verify them with executable exploits, and remediate them with tested code fixes. </span></p> <p><span style="vertical-align: baseline;">“At Salesforce, trust is our number one value, and protecting customer data means continually raising the bar for how we find, validate, and mitigate risks. CodeMender brings AI into a critical part of the security lifecycle by accelerating the path from validated vulnerability to tested fix. As AI reshapes the threat landscape, capabilities like this help strengthen resilience and give our customers the confidence to keep innovating,” said Iain </span><span style="vertical-align: baseline;">Mulholland, CISO, Salesforce</span><span style="vertical-align: baseline;">.</span></p> <p><span style="vertical-align: baseline;">"CodeMender consistently identified critical vulnerabilities that our other AI-enabled tools completely missed. It doesn't just find theoretical flaws — it proves the immediate risk and delivers targeted, validated fixes that secure our environment without disrupting core business logic," said Scott Ponte, head, Security Operations, Robinhood. </span></p> <p><span style="vertical-align: baseline;">"CodeMender is fast, comprehensive, and genuinely ambitious about closing the loop from detection to fix, enabling teams to secure their software supply chain without losing velocity," said Ashwin Kannan, principal AI engineer, Office of the CTO, Palo Alto Networks.</span></p> <h3><strong style="vertical-align: baseline;">How the CodeMender agent works</strong></h3> <p><span style="vertical-align: baseline;">We’ve fine-tuned CodeMender’s harness to be continuously updated with the latest Google DeepMind research, including the up-to-date agent skills, security tools, and system prompts. </span></p> <p><span style="vertical-align: baseline;">Operating in the secure-by-design Agent Platform, CodeMender is protected by enterprise-grade, built-in governance and security guardrails, including secure traffic routing through your VPC, data isolation and encryption, and zero retention of source code data.</span></p> <p><span style="vertical-align: baseline;">As an agent, it can integrate with existing continuous integration and continuous delivery (CI/CD) workflows, or run directly in local developer environments using a lightweight command-line interface (CLI) client. </span></p> <p><span style="vertical-align: baseline;">You can also configure CodeMender to scan and analyze code in a sandbox that you manage. The agent connects to your code repositories and works with developer tools, such as </span><a href="https://docs.cloud.google.com/code/docs/vscode/install"><span style="text-decoration: underline; vertical-align: baseline;">VS Code</span></a><span style="vertical-align: baseline;"> and </span><a href="https://antigravity.google/" rel="noopener" target="_blank"><span style="text-decoration: underline; vertical-align: baseline;">Antigravity</span></a><span style="vertical-align: baseline;">, to safely analyze first-party, open-source, and third-party software.</span></p> <h3><strong style="vertical-align: baseline;">Scan: Find hidden vulnerabilities with flexible model scanning </strong></h3> <p><span style="vertical-align: baseline;">CodeMender scans for top vulnerability classes and understands the </span><a href="https://cloud.google.com/blog/products/identity-security/cloud-ciso-perspectives-how-ai-leverages-deep-context-defenders-advantage"><span style="text-decoration: underline; vertical-align: baseline;">unique context, goals, and functionality</span></a><span style="vertical-align: baseline;"> of your software repositories and applications.</span></p></div> <div class="block-image_full_width"> <div class="article-module h-c-page"> <div class="h-c-grid"> <figure class="article-image--large h-c-grid__col h-c-grid__col--6 h-c-grid__col--offset-3 " > <img src="https://storage.googleapis.com/gweb-cloudblog-publish/images/2_LNSezkk.max-1000x1000.png" alt="2"> </a> <figcaption class="article-image__caption "><p data-block-key="c7u8w">Scan: Discovered new vulnerabilities and categorized by severity and type.</p></figcaption> </figure> </div> </div> </div> <div class="block-paragraph_advanced"><p><span style="vertical-align: baseline;">CodeMender’s harness with security context helps you discover sophisticated vulnerabilities that static and model-only scanning miss. These scans look for hard-to-find vulnerabilities like memory corruption, injection, web security issues, cryptographic flaws, and insecure data handling. CodeMender supports common software languages including C/C++, Go, Java, Python, Ruby, Rust, and TypeScript.</span></p> <h3><strong style="vertical-align: baseline;">Verify: Simulate and verify exploits to reduce noise</strong></h3> <p><span style="vertical-align: baseline;">CodeMender can help cut alert fatigue and false positives by proving a vulnerability presents a legitimate risk before fixing it. The agent goes beyond static code-pattern analysis by simulating an attack with exploit code it builds and runs in an isolated, customer-managed sandbox.</span></p></div> <div class="block-image_full_width"> <div class="article-module h-c-page"> <div class="h-c-grid"> <figure class="article-image--large h-c-grid__col h-c-grid__col--6 h-c-grid__col--offset-3 " > <img src="https://storage.googleapis.com/gweb-cloudblog-publish/images/3_OEvpSjA.max-1000x1000.png" alt="3"> </a> <figcaption class="article-image__caption "><p data-block-key="c7u8w">Verify: Creates verification plan and builds and tests exploits in your sandbox environment.</p></figcaption> </figure> </div> </div> </div> <div class="block-paragraph_advanced"><p><span style="vertical-align: baseline;">The agent uses this proof-of-concept exploit to verify that the security flaw poses a legitimate risk. This critical verification phase allows your security practitioners and developers to prioritize validated risks by eliminating false positives.</span></p> <h3><strong style="vertical-align: baseline;">Remediate: Automatically generate and test code fixes</strong></h3> <p><span style="vertical-align: baseline;">Identifying risky security flaws is only half the battle. Once a vulnerability is verified, CodeMender automatically generates a secure patch to resolve the issue. The fix is delivered as a code difference directly in developer tools, so it can be integrated into existing development workflows.</span></p></div> <div class="block-image_full_width"> <div class="article-module h-c-page"> <div class="h-c-grid"> <figure class="article-image--large h-c-grid__col h-c-grid__col--6 h-c-grid__col--offset-3 " > <img src="https://storage.googleapis.com/gweb-cloudblog-publish/images/4_LlL5FCA.max-1000x1000.png" alt="4"> </a> <figcaption class="article-image__caption "><p data-block-key="c7u8w">Remediate: Generates and tests code fix with code diff for developer review and approval.</p></figcaption> </figure> </div> </div> </div> <div class="block-paragraph_advanced"><p><span style="vertical-align: baseline;">CodeMender further strengthens the fix by using LLM-as-a-judge to ensure it doesn’t disrupt existing application functionality. You can even provide context on your codebase's distinct coding conventions and styles so that CodeMender generates code that matches it. Developers remain in full control, manually reviewing and approving CodeMender's patches before any code is committed to the repository.</span></p> <h3><strong style="vertical-align: baseline;">CodeMender in AI Threat Defense</strong></h3> <p><span style="vertical-align: baseline;">When leveraged as part of </span><a href="https://cloud.google.com/security/ai-threat-defense"><span style="text-decoration: underline; vertical-align: baseline;">AI Threat Defense</span></a><span style="vertical-align: baseline;">, Wiz orchestrates agentic application security, analyzing applications to prioritize investigations. It calls CodeMender to scan code (coming soon), enrich findings within the </span><a href="https://www.wiz.io/lp/wiz-security-graph" rel="noopener" target="_blank"><span style="text-decoration: underline; vertical-align: baseline;">Wiz Security Graph</span></a><span style="vertical-align: baseline;"> with deployment context, and trigger </span><a href="https://www.wiz.io/solutions/red-agent" rel="noopener" target="_blank"><span style="text-decoration: underline; vertical-align: baseline;">Wiz Red Agent</span></a><span style="vertical-align: baseline;"> for AI pentesting to prove exploitability, ensuring that teams focus on the highest-risk vulnerabilities.</span></p></div> <div class="block-image_full_width"> <div class="article-module h-c-page"> <div class="h-c-grid"> <figure class="article-image--large h-c-grid__col h-c-grid__col--6 h-c-grid__col--offset-3 " > <img src="https://storage.googleapis.com/gweb-cloudblog-publish/original_images/AITD_Wheel_-_Copy_of_Final_-_BLOG-ALT_AIThreatChart_2436x1200_v2.gif" alt="AITD Wheel - Copy of Final - BLOG-ALT_AIThreatChart_2436x1200_v2"> </a> <figcaption class="article-image__caption "><p data-block-key="r3bx6">Through Wiz, AI Threat Defense calls CodeMender to scan code, enrich findings, and trigger AI pentesting.</p></figcaption> </figure> </div> </div> </div> <div class="block-paragraph_advanced"><p><span style="vertical-align: baseline;">Wiz serves as a command center for governing and scaling remediation in AI Threat Defense. The </span><a href="https://www.wiz.io/blog/introducing-wiz-green-agent" rel="noopener" target="_blank"><span style="text-decoration: underline; vertical-align: baseline;">Wiz Green Agent</span></a><span style="vertical-align: baseline;"> orchestrates this lifecycle by directing CodeMender to generate and test high-fidelity patches enriched with application context from the Security Graph. This </span><a href="https://www.wiz.io/blog/introducing-wiz-workflows" rel="noopener" target="_blank"><span style="text-decoration: underline; vertical-align: baseline;">workflow</span></a><span style="vertical-align: baseline;"> empowers teams to resolve complex vulnerabilities with unprecedented speed and precision.</span></p> <h3><strong style="vertical-align: baseline;">How to get started with CodeMender</strong></h3> <p><span style="vertical-align: baseline;">Consistent with our </span><a href="https://cloud.google.com/blog/products/identity-security/cloud-ciso-perspectives-next-26-why-we-re-multicloud-and-multi-ai"><span style="text-decoration: underline; vertical-align: baseline;">multi-model approach</span></a><span style="vertical-align: baseline;">, CodeMender can help you optimize for cost, speed, and deep scanning performance.</span></p> <p><span style="vertical-align: baseline;">You can use CodeMender with our generally available Gemini models via </span><a href="https://docs.cloud.google.com/gemini-enterprise-agent-platform/codemender"><span style="text-decoration: underline; vertical-align: baseline;">Agent Platform</span></a><span style="vertical-align: baseline;">, or deploy it as a core component of </span><a href="https://cloud.google.com/security/ai-threat-defense"><span style="text-decoration: underline; vertical-align: baseline;">AI Threat Defense</span></a><span style="vertical-align: baseline;">.</span></p> <p><span style="vertical-align: baseline;">Separately, CodeMender with </span><a href="https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-6-flash-3-5-flash-lite-3-5-flash-cyber/" rel="noopener" target="_blank"><span style="text-decoration: underline; vertical-align: baseline;">Gemini 3.5 Flash Cyber</span></a><span style="vertical-align: baseline;"> will be exclusively available to a small set of governments and trusted partners. We plan to expand this access over time.</span></p> <p><span style="vertical-align: baseline;">CodeMender is a critical step towards a continuous, self-healing agentic software development lifecycle, a future where code is autonomously secured, validated, and patched before it ever hits production. </span></p> <p><span style="vertical-align: baseline;">You can learn more about CodeMender and review the documentation </span><a href="https://docs.cloud.google.com/gemini-enterprise-agent-platform/codemender"><span style="text-decoration: underline; vertical-align: baseline;">here</span></a><span style="vertical-align: baseline;">. </span></p></div> Maestro News, gelişmeyi Maestro Dev ekseninde — yazılım, yapay zekâ, bulut ve ürün teslimatı — özgün dilde yeniden çerçeveliyor.
