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The ChatGPT-maker said training will be slowed for two weeks while it puts the upgrades in place.
Flock’s surveillance cameras have already sparked outrage. WIRED reconstructed its next-generation AI system, already in use by some police, to confirm it goes much further than tracking license plates.
After a stunning debut last week with cyber capabilities so advanced they reportedly found a previously undetected vulnerability in Cursor, GLM-5.3, the new frontier open source language model from Chinese startup z.ai, has now hit the application programming interface (API) — allowing developers the ability to build atop it and plug it into their agents and applications. Developers who previously subscribed to a GLM Coding Plan are currently limited to the OpenAI Chat Completions-compatible protocol. Z.ai said it plans to make the model's weights openly available, but a precise date and licensing remain to be seen. On the API, the price is unchanged from GLM-5.2: $1.40 per million input tokens and $4.40 per million output tokens . Cached input costs $0.26 per million tokens, while Z.ai currently lists cached-input storage as free for a limited time. That means developers can move to the new generation without taking a higher posted per-token rate from Z.ai, even as the company claims substantially stronger coding and long-horizon agent performance. At those rates, GLM-5.3 sits well below several of the highest-end frontier APIs. Model Input ($/1M) Output ($/1M) Total ($/1M) Source Muse Spark 1.2 Contributor $0.10 $0.20 $0.30 Meta MiMo-V2.5 Flash $0.10 $0.30 $0.40 Xiaomi DeepSeek-V4-Flash — off-peak $0.22 $0.66 $0.88 DeepSeek GPT-5.6 Luna $0.20 $1.20 $1.40 OpenAI MiniMax-M3 $0.30 $1.20 $1.50 MiniMax LongCat-2.0 — limited-time promo $0.30 $1.20 $1.50 LongCat DeepSeek-V4-Flash — peak hours $0.44 $1.32 $1.76 DeepSeek MiMo-V2.5 $0.40 $2.00 $2.40 Xiaomi DeepSeek-V4-Pro — off-peak $0.66 $1.98 $2.64 DeepSeek LongCat-2.0 — standard $0.75 $2.95 $3.70 LongCat MiMo-V2.5 Pro (≤256K) $1.00 $3.00 $4.00 Xiaomi Gemini 3.6 Flash — through Dec. 31, 2026 $0.75 $3.75 $4.50 Google Gemini 3.7 Flash — through Dec. 31, 2026 $0.75 $3.75 $4.50 Google DeepSeek-V4-Pro — peak hours $1.32 $3.96 $5.28 DeepSeek Muse Spark 1.1 / 1.2 $1.25 $4.25 $5.50 Meta GLM-5.3 $1.40 $4.40 $5.80 Z.AI Grok 4.6 — $2.00 $6.00 $8.00 xAI MiMo-V2.5 Pro (>256K) $2.00 $6.00 $8.00 Xiaomi Qwen3.8-Max $2.00 $6.00 $8.00 QwenCloud Gemini 3.6 Flash — starting Jan. 1, 2027 $1.50 $7.50 $9.00 Google Gemini 3.7 Flash — starting Jan. 1, 2027 $1.50 $7.50 $9.00 Google GPT-5.6 Terra $2.00 $12.00 $14.00 OpenAI Grok 4.6 — ≥200K prompt tokens $4.00 $12.00 $16.00 xAI GPT-5.4 $2.50 $15.00 $17.50 OpenAI Kimi K3 $3.00 $15.00 $18.00 Moonshot AI Claude Opus 5 $5.00 $25.00 $30.00 Anthropic Sakana Fugu Ultra (≤272K) $5.00 $30.00 $35.00 Sakana AI GPT-5.6 Sol — Standard mode $5.00 $30.00 $35.00 OpenAI Claude Fable 5 / Claude Mythos 5 $10.00 $50.00 $60.00 Anthropic GPT-5.6 Sol — Fast mode $10.00 $60.00 $70.00 OpenAI Using the simple VentureBeat comparison of one million input tokens plus one million output tokens, GLM-5.3 comes to $5.80, versus $8 for Grok 4.6 at its lower context rate, $18 for Kimi K3, $30 for Claude Opus 5 and $35 for GPT-5.6 Sol . That is not a workload-cost estimate — real bills depend heavily on the input/output mix, caching and token consumption — but it makes the relative API price tier easy to see. GLM-5.3 is not the cheapest capable model available. Google’s current introductory price for Gemini 3.7 Flash is $0.75 per million input tokens and $3.75 per million output tokens through Dec. 31, 2026, while OpenAI’s GPT-5.6 Luna is priced at $0.20 input and $1.20 output. Still, Z.ai’s price puts GLM-5.3 into a notably lower cost band than the premium frontier models it is increasingly benchmarked against. That comparison has become more relevant following the latest independent results. Artificial Analysis gives GLM-5.3 a score of 60 on its Intelligence Index , tying Kimi K3 as the top performing open weights model in the world, and scoring seven points higher than GLM-5.2. Its analysis also estimates GLM-5.3 at about $0.68 per Intelligence Index task, versus roughly $0.44 for GLM-5.2, despite the identical API token prices. The difference underscores an important caveat in headline API pricing: Artificial Analysis found GLM-5.3 more verbose than its predecessor, so flat per-token rates do not necessarily mean flat costs for a completed workload. For developers, though, the immediate change is straightforward: GLM-5.3 is now callable through Z.ai’s API at the same $1.40/$4.40 per-million-token rate as GLM-5.2, giving teams another relatively low-cost option for testing frontier-class coding and agent workloads.
Block , the technology company founded by former Twitter CEO Jack Dorsey that owns Square, Cash App and the music streaming service Tidal, is open-sourcing Berd , a desktop application it originally built to give its own employees a single environment for working with AI agents across different models, tools and projects. Berd is a locally installed graphical desktop application rather than a browser-based workspace. It is available now on GitHub under a permissive Apache 2.0 license — meaning anyone can use, modify and redistribute it, including commercially — with free downloadable builds for macOS, Windows and Linux. The repository reached version 0.6.2 on Aug. 18, its seventh public release, and lists 91 contributors. “Berd is desktop-first because much of its value comes from working directly with projects, local files, tools, repositories, and agents running on or connected to the user’s computer,” Brad Axen, Head of AI Capabilities at Block, told VentureBeat in emailed responses. A daily work surface for AI agents Block positions Berd as a “daily AI work surface”: a single place where users can start chats, attach files or folders, choose agents and models, work inside persistent projects, configure AI providers, manage skills and extensions, review session history and build automations. The design goal is not merely convenience. According to Berd’s product specification, users should always be able to tell which project, files, agent, model, provider and session state are actively shaping a conversation. That emphasis on visible operational state is what Block says separates Berd from a generic chatbot wrapper. Configuration — providers, extensions, skills, automations, projects — is treated as part of the workflow rather than buried in an administrative layer, and the interface is designed to expose failures, unavailable providers, and loading and streaming states plainly rather than smoothing them over with assistant-style friendliness. Berd grew out of a practical problem inside Block. Employees were already working with capable agents — Block’s own Goose, Anthropic’s Claude Code and OpenAI’s Codex — but the experience around them had become fragmented. “We had capable agents through goose, Claude Code, and Codex, but working across them meant navigating different interfaces, configuration systems, and ways of managing context,” Lucinda Bell, Technology Communications at Block, said in an email. Berd, she said, gives Block teams “one consistent desktop application across models and harnesses.” For enterprises, that layer may matter as much as another incremental gain in model capability. Persistent projects mean users can return to an established collection of files, instructions and agent configurations instead of reconstructing context for each task. Block also explicitly designed Berd to make agentic work accessible beyond engineering, letting people start with a conversation and add tools, context and structure as the work requires. Block is not positioning Berd as a mobile product. Axen said the company’s open source collaboration platform Buzz already provides a mobile experience and is Block’s preferred environment for work on the go. Block thinks agents should look different when they are different Berd takes an unusual approach to a problem most enterprise AI products leave implicit: how users tell one configured agent apart from another. Instead of treating every agent as another blank chat window, Berd gives agents roles, instructions, skills, tools and recognizable visual identities. Block created collections of animated characters — its flagship designs are called “Gloopies” — and Berd’s public site showcases predefined personas such as Berdy, Pushback, Choosey, Copycat, Tinker and Wildcard, each built around a different style of work: Pushback plays devil’s advocate on drafts, Choosey helps narrow decisions, Copycat learns to write in the user’s style. The visual layer is not meant to substitute personality for substance. Block’s own formulation, from a company blog post on Berd’s design , is functional: “The avatars make the agent recognizable. Its role, skills, and tools make it useful.” That distinction could grow more important as workers juggle several specialized agents at once. A recognizable identity serves as visible shorthand for an otherwise invisible package of instructions, tools and permissions — agents appear on the workspace as distinct animated characters rather than identical entries in a chat list, so users can tell them apart before opening a conversation. The product specification guards against the characters turning Berd into a novelty. It describes the intended personality as “focused, capable, companionable,” while explicitly rejecting toy-like presentation, oversized decorative UI and interfaces that hide operational state behind friendliness. An orchestration layer, not another model or agent runtime Architecturally, Berd is neither a new foundation model nor a new agent runtime. The application is built with Tauri 2 and React 19 — frameworks for building lightweight desktop apps with web technologies — and communicates with Goose, the open source agent framework Block introduced in January 2025, through a standard called the Agent Client Protocol (ACP). Goose runs quietly alongside Berd as a bundled “sidecar” process and handles the underlying agent loop; Berd handles projects, sessions, context, agents and configuration on top. For readers unfamiliar with it, Goose is a model-agnostic agent framework and runtime, originally focused on software development workflows. It connects large language models to files, commands and outside systems — including through the Model Context Protocol (MCP), an open standard for wiring AI systems to external tools and data — so agents can take actions rather than merely generate text. Goose works with proprietary model providers as well as locally run open models. Block has since contributed Goose to the Agentic AI Foundation , the Linux Foundation body it helped establish with Anthropic, OpenAI and others in December 2025, giving the framework a vendor-neutral home alongside MCP. Axen drew the boundary explicitly: agent harnesses such as Goose, Claude Code and Codex manage the loop between a model, its context and its tools, while Berd provides the consistent desktop environment around those harnesses. In the public release, users configure whichever providers and harnesses they want rather than inheriting a Block-selected model stack. The project’s README adds an operational detail that matters for anyone evaluating the software: Berd does not simply pull whatever Goose build happens to be current. Its build process pins a specific Goose backend version through a lockfile, verifies the cached binary matches that pinned version, and packages it with the app. Developers can explicitly substitute another Goose binary for local testing. For organizations inspecting Berd, that creates a defined version boundary between the desktop application and its default agent backend rather than letting the backend silently drift. How Berd compares to Codex, Claude Code, Cursor and Goose That separation matters because the agent-tooling market is already converging on products that manage increasingly autonomous AI workers. OpenAI describes its Codex app as a “command center” for agents, with separate project threads and multiple agents operating in parallel. Claude Code , Anthropic’s agent for reading codebases, editing files and executing commands, now spans the terminal, IDEs and a desktop app. Cursor describes its latest release, Cursor 3, as a unified workspace for software development with agents, including handoffs between local and cloud agents and multi-repository workflows. And Goose itself remains available directly, as a free command-line tool and desktop app for technically comfortable users. Berd’s differentiation is therefore less about inventing the agent workspace than about making that workspace open source, multi-harness and potentially useful outside coding. Every other product in the table below is either tied to a single vendor’s models, priced as a subscription, or aimed squarely at developers — and in most cases all three. Product Maker Pricing Model support Distinguishing features Berd Block Free and open source (Apache 2.0); users pay only their own model provider costs Any provider the user configures, across multiple agent harnesses Desktop workspace over agent harnesses rather than a harness itself; local-first data storage; visual agent identities; persistent projects; enterprise custom distributions; designed for non-engineers as well as developers Codex app OpenAI Included with paid ChatGPT plans, from Plus at $20/month to Pro at $200/month OpenAI models only “Command center” for coding agents; multiple agents running in parallel across separate project threads; cloud and local execution Claude Code Anthropic Included with paid Claude plans, from Pro at $20/month to Max at $100–$200/month; also usage-based via API Anthropic Claude models only Deep autonomous coding — reads codebases, edits files, runs commands; available in terminal, IDEs, desktop and web; usage caps have drawn developer criticism Cursor Anysphere Pro at $20/month; Ultra at $200/month Multiple frontier models AI-native code editor; Cursor 3 adds a unified agent workspace with local-to-cloud agent handoffs and multi-repository workflows; developer-focused Goose Block / Agentic AI Foundation Free and open source (Apache 2.0); bring your own model, including free local models Any provider, or fully local models via tools like Ollama The model-agnostic agent framework and runtime underneath Berd; CLI and desktop versions; can run entirely offline with no data leaving the machine Portability is part of Block’s pitch. Axen said much of the underlying work in Berd is deliberately kept portable: folders, Git repositories, files and plain-text instructions remain accessible outside the application; skills are file-based; and Berd agents can be exported and shared between Berd users with their instructions and settings intact. He pointed to ACP and MCP as part of Block’s direction toward “clear, user-owned formats and open protocols wherever possible.” That does not make every piece of state interchangeable across every harness, but it makes portability an explicit architectural goal rather than a side effect of using local files. Local-first data, telemetry and enterprise distributions Berd is designed around a local-first data model. Axen said conversation history is stored on the user’s device in the local Goose session database, while credentials live in the operating system keychain by default. That does not mean all AI processing happens locally. “Local-first does not mean that no data ever leaves the device,” Axen said. When a user asks a configured model to work on particular context, the relevant prompt and material are sent to that model’s provider — so the actual data path, and the contractual terms governing it, depend on which provider an organization chooses. Telemetry — the usage data software sends back to its maker — is disabled by default in official Berd distributions. If a user opts in, Block says it collects a random installation identifier and a limited set of predefined usage events, while excluding prompts, messages, files, source code, credentials, names, email addresses and IP addresses from the telemetry payload itself (Block and its service providers still process IP addresses for transmission, security and rate limiting). Users can switch telemetry off again in settings. Block cautions that third-party forks can modify or redirect the telemetry system, so enterprises evaluating a customized build need to review that build’s actual behavior rather than assume Block’s defaults carry over. The public repository builds a self-contained, general-purpose distribution that does not depend on private package registries or enterprise credentials — technical teams can build and evaluate it without access to Block’s infrastructure. But Block has also created what it calls “distribution seams”: defined points where organizations can overlay managed provider settings, private agents, runtime configuration, optional companion tools, their own update channels and their own signing and publishing infrastructure, all without adding private material to the public source tree. Block’s own deployment shows how an enterprise can put governance around that architecture. Axen said Block runs its managed distribution on approved devices using approved model-provider infrastructure, managed configuration and governed tool connections. “The application can remain open and inspectable, while an organization decides which models, tools, permissions, and policies are appropriate for its environment,” he said. One caveat: “enterprise distribution” should not be read as a complete enterprise management plane. The README does not describe capabilities such as single sign-on, centrally enforced administrator policies, organization-wide permission controls, data-retention rules or audit logs. It establishes that companies can customize and distribute Berd; it does not by itself explain how those installations are centrally governed after deployment. Berd should be understood as endpoint software installed on users’ computers, not a centrally hosted SaaS product. Pricing, models and a closed contribution model Block has not announced a subscription price for Berd, and there is no indication one is coming. That does not make agent usage costless: organizations still pay whatever model, API or provider charges they incur. The economic pitch on Berd’s site is flexibility — users can match the model to the job rather than paying for an expensive frontier model on every task. As the site puts it: “Some tasks need the big brain. Most don’t.” Neither Block’s announcement nor the README provides a definitive matrix of supported model providers or recommends particular models. Internally, Block does not treat any one model as a permanent default: Axen said the company’s managed distribution exposes employees to “a curated mix of frontier and open-weight models,” with the catalog changing as models improve. “The best choice depends on the work,” he said. There is also a governance distinction developers should note. Although Berd’s source is public and Block solicits issues and feedback, the repository does not accept outside pull requests — external code contributions are automatically closed. The README says Berd is built by “a small team at Block” and directs outside participants toward well-formed, reproducible bug reports instead. Users are free to inspect, build and fork the code, but Block retains a tightly controlled upstream. For companies considering Berd as infrastructure rather than an experiment, that makes Block’s long-term maintenance and support plans worth clarifying. Block is not presenting Berd as a revenue product today. Axen said the first return on investment comes from Block’s own use of the software — making internal agent work more consistent while teaching the company how employees actually use agents — and that open-sourcing it adds leverage by letting outsiders inspect, adapt and give feedback on the architecture. A commercial layer around enterprise deployment may eventually follow. “Supporting organizations that want to deploy agents at work is an area of interest for us,” Axen said, adding that Block will share more when there is more to share. From private agent work to multiplayer workflows Berd supplies the individual desktop experience; another Block project, Buzz , focuses on shared rooms where people and agents collaborate against a common, searchable record. Buzz is Block’s free, Apache 2.0-licensed collaboration workspace for humans and AI agents. Built on the decentralized Nostr protocol, it combines familiar team features — channels, threads, direct messages, voice and media sharing — with agents that hold their own cryptographic identities and defined permissions, and that can participate in conversations, code review and approved automated workflows. Unlike Berd’s local, single-user focus, Buzz is explicitly multiplayer, and can be self-hosted or used through Block-hosted infrastructure. It is likewise model- and harness-agnostic: teams can bring agents powered by Goose, Claude Code, Codex or others. Axen framed the split between the two products partly as a question of trust boundaries. “A local agent may have access to your filesystem and credentials,” he said. “An agent in a shared Buzz room has its own identity and access scoped through the relay.” Berd begins with one person and comparatively privileged access to that person’s machine; Buzz begins with multiple participants, explicit identities and shared-workspace permissions. The more consequential roadmap detail is that Block does not intend the two products to remain separate. “Going forward, our focus is Buzz, and we plan to bring the best parts of Berd’s single-player experience into it,” Axen said. Block wants work to begin privately and become collaborative without users switching tools or rebuilding context, with cross-device continuity — including mobile via Buzz — part of that ambition. In the meantime, Axen said, Berd remains available as an open source desktop application, but Buzz is the product Block encourages people to download and adapt. What enterprise adopters should take away The short version for IT and security teams: conversation history stays local by default; credentials use the operating system keychain; context sent to a model leaves the machine on whatever terms the configured provider offers; official telemetry is off by default and excludes prompts, files and source code; and organizations can package their own governed distribution — though central management tooling is not yet documented. The larger idea behind the release may matter more. Models and coding agents are becoming increasingly interchangeable components inside larger workflows. Berd is Block’s bet that the surrounding context — the projects, skills, tools, identities and persistent working environment — becomes the stable layer instead.
The ChatGPT maker says its upcoming Astra model may have reached “critical” cyber capabilities, prompting it to halt a significant number of training runs while it tightens internal safeguards.
Enterprises that already got burned by an AI agent passing its evals and then failing in production are moving faster toward removing humans from deployment decisions, not slower — even as trust in automated evaluation is rising across the board, new VB Pulse research shows . In July, 13% of 108 enterprises surveyed said they trust automated evaluation, up from just 5% the month prior . Meanwhile, survey respondents citing poor alignment between tests and real-world results as their biggest concern fell 10 points, from 29% to 19%, month over month. Yet, 49% of survey respondents said that an AI agent or LLM-powered feature that had cleared company testing subsequently created a problem visible to customers , essentially unchanged from 50% in June. And nearly a quarter, 24%, said this troubling outcome had occurred more than once. The latest findings from VentureBeat Intelligence uncovered a more troubling phase of the enterprise agent rollout: the gap is no longer only between how much autonomy companies give agents and how well they can verify them. It is increasingly a gap between confidence in the evaluation layer and evidence that the layer is getting better at preventing failures. The most revealing split appears inside the July data. Of the enterprises that experienced an AI feature clear testing only to go on to disappoint a customer, 4% placed complete faith in automated checks. Of those that had detected no comparable incident, 24% expressed full confidence — a sixfold difference. It makes sense: those who experienced test-passing agents failing in live production are, unsurprisingly, more likely to doubt the automated checking process. Perhaps it makes sense then, that companies geared toward tackling this problem — like automated agent error monitoring and mitigation platform Raindrop.ai — are seeing the market transform wildly from just a few months ago. "We are seeing the great-decline of evals as we know them," Raindrop CTO Ben Hylak told VentureBeat in a direct message. "The Fortune 100 are increasingly reducing eval sets and deprioritizing maintenance. As systems grow more complex (MCPs, subagents, etc.) it becomes impossible to fully enumerate the failure cases. Instead, they’re leaning on anomaly and issue detection solutions, both before and after production." A directional finding, not a market census VentureBeat fielded the July wave among 108 people representing companies with workforces of at least 100. This is down from 157 respondents in June. Of the 108, 69% described themselves as final AI-buying authorities or people who recommend and influence those purchases. The sample skewed toward midsize organizations: 63% worked at companies with 100 to 2,499 employees. The findings should be read directionally. The survey is self-selected rather than a probability sample, and the burned-vs.-unburned splits cited throughout this piece rest on groups of 41 to 53 respondents, and other cross-tabs in the report range from 40 to 68. The industry mix also changed: technology and software participation declined nine points, ending at 14%, while the retail and consumer share added four points and ended at 19%. The report nevertheless identifies four month-to-month changes worth noticing: more respondents professing complete confidence, fewer naming poor real-world alignment, more choosing integration ease as the decisive buying factor, and Braintrust gaining primary-platform share. Confidence in automated evals improved, but outcomes stayed flat VentureBeat's June research identified an enterprise evaluation gap : companies were granting agents more authority faster than they were developing reliable ways to test them. July preserves the key number from that first wave. Across 265 enterprise responses over the two months, the proportion reporting at least one test-approved system that disappointed customers stayed within a single percentage point : 50% in June and 49% in July. This figure does not mean that 49% of all agent runs fail, or that any particular evaluation product has a 49% failure rate. The survey asks whether an organization experienced at least one customer-facing incident in the previous year after an AI feature passed its internal tests. Companies that deploy far more agents have more opportunities to encounter such an incident. But that limitation does not make the result less important. An internal evaluation serves as a release gate. If roughly half of surveyed organizations have seen that gate approve a system that later fails in front of customers, a passing score cannot be treated as proof of production reliability. The cross-tab reinforces the point. Ten of the 41 enterprises with no identified testing miss placed complete faith in automation. Only two of the 53 previously burned enterprises said the same. Confidence is strongest among respondents with the least evidence that the release gate can fail. The enterprises that got burned are moving faster toward zero-human deployment The counterintuitive finding is what companies do after an evaluation miss. Overall, 67% either let an agent push code or change a system without a person's approval in certain low-risk cases, or are modifying their pipelines to support that practice during the coming year . That is unchanged from June. In July, 37% already permitted it in limited cases and another 30% were building toward it. Among enterprises where a test-approved system had disappointed a customer, however, 85% were pursuing that no-approval model , compared with 61% in the group reporting no comparable incident. Only 11% of burned respondents rejected end-to-end deployment automation for the years ahead, versus 24% of unburned respondents. It would be easy to read that as recklessness, but the data supports another plausible explanation: deployment maturity. Organizations running more agents, at higher volume and across more consequential workflows, are more likely both to encounter failures and to have the engineering infrastructure needed for automated deployment. The survey cannot establish which explanation dominates. It does establish that a customer-visible incident does not appear to stop the move toward autonomy. Respondents with firsthand proof that testing can miss defects are also moving most aggressively to let those tests authorize production changes. If their per-deployment failure rate remains constant while deployment volume rises, the total incident count could grow even without the percentage of affected companies increasing. The July data does not measure incident volume, so that remains a risk implied by the pattern rather than a measured outcome. The release gate is automated, but production quality monitoring still lags Pre-deployment evaluation and production monitoring answer different questions. An evaluation asks whether an agent appears ready to ship. Production monitoring asks what the agent is doing after release and whether its live outputs remain correct. Most companies in the July sample still emphasize whether the system functions, not whether the answer is correct. Among the 106 valid responses to this question, 26% used inline quality assertions — automated judges or guardrails checking live traffic for output-quality problems. Another 26% focused on transaction traces such as infrastructure spans, token usage and raw inputs and outputs, while 24% mainly tracked gateway metrics such as latency, errors and cost. Trace and gateway data can reveal outages, slowdowns and broken requests. They may not flag a fluent, fast and confidently wrong answer. Grouped by the report according to what each architecture actually watches, half of respondents monitored whether an agent was functioning, while just over a quarter automatically monitored whether its production output was correct. The gap is sharpest among the 40 respondents already permitting no-approval deployment in limited cases. Only 28% of that group automatically checked the meaning and correctness of live answers . In other words, most enterprises that have eliminated a person from at least some release decisions have not installed automated semantic-quality monitoring as the production backstop. This is the clearest operational lesson in the data. A pre-deployment test suite and infrastructure observability are necessary, but they do not cover the same failure mode. Enterprises need a way to detect bad outputs after the agent begins interacting with real users, data and tools — especially when nobody reviews the deployment decision first. An independent agent-evaluation market begins to take shape The vendor data offers a more encouraging sign: enterprises are adding dedicated evaluation tools, and specialist platforms are gaining ground. OpenAI's native evals and traces narrowly led as the primary platform at 18%, followed by Confident AI's DeepEval at 17% and Braintrust at 15% . Anthropic's Claude Console and Workbench held 12%, tied with organizations reporting no dedicated evaluation platform. Three options each held 6%: internally built tools, Promptfoo and LangSmith. Braintrust's primary share increased from 8% in June to 15% in July, the biggest gain by one vendor and the one the report flags as statistically significant. DeepEval rose from 12% to 17%. Use of no purpose-built platform declined five points to 12%, although that smaller change does not by itself confirm a trend. Because many companies use more than one tool, the broader footprints are larger. OpenAI native evaluation appeared somewhere in 31% of stacks, DeepEval in 27%, Braintrust in 22% and Anthropic's native tooling in 20%. Custom internal tooling reached 14%, while Weights & Biases Weave and open-source Langfuse each reached 11%. These are adoption figures, not product-performance scores. The survey does not establish that one vendor produces more reliable agents than another. Still, the results point toward evaluation becoming a distinct enterprise software layer rather than a loose collection of internal scripts or a feature used only inside a model provider's platform. Purchasing priorities are changing with that market. The proportion choosing integration ease as the decisive factor climbed 12 points to 39% , displacing cost, which fell from 28% to 23%. Evaluation accuracy ranked second at 28%. The combined average for satisfaction, implementation simplicity and economic value was 3.9 out of five. The move from price toward integration suggests enterprises increasingly want a tool they can install into existing development and monitoring pipelines now. Yet their leading success metric remains evaluation consistency at 38%, followed by fewer failures and regressions at 20%. Buyers are selecting for fit while still judging results on repeatability. Switching intent also cooled: 56% still expected to add or replace a platform during the coming year, down from 64% in June. The proportion staying put increased eight points, reaching 44%. Together with specialist adoption, they suggest some buyers are moving from evaluation to implementation. Human review is becoming the hedge against automated misses The budget data reveals how enterprises are managing the contradiction between greater autonomy and unreliable evaluation. People-centered review workflows edged narrowly ahead of production observability as the most frequently cited area for increased investment, 31% to 30%. Automated evaluation pipelines ranked third at 19%, followed by testing for safety and policy compliance at 16%. Only 6% said their reliability and evaluation budget was not increasing. Among enterprises that had experienced a testing miss, 38% said people-centered review would receive the fastest investment growth, compared with 24% of organizations that had not been burned. That produces an apparent paradox: the burned group is most likely to remove people from the release checkpoint and most likely to increase spending on people elsewhere in the process. The strategy appears to be automation with a human backstop — allow agents to move faster, then use reviewers to catch what automated evaluation misses. The open question is whether that model scales. Agent deployments and automated checks can grow with software volume. Reviewer hours do not fall at the same rate. Enterprises may therefore be replacing a human approval step with a larger downstream review function rather than eliminating human oversight. The narrow but consequential read July's data does not show that enterprise agent evaluation is failing everywhere, nor does it prove automated judges are getting worse. It shows something more precise: confidence rose before the measured failure incidence improved . At the same time, the infrastructure around evaluation is maturing. More enterprises are adopting specialist tools, integration has become the leading purchase criterion and companies that have already experienced failures are increasing investment in human review. The market recognizes the problem and is spending against it. But the central reliability result remains stubborn. Nearly half of surveyed enterprises still report that an AI feature cleared internal checks before disappointing a customer. Respondents with that experience place less faith in automation — and move faster toward deployments with no human approval. The report frames this as an incomplete verification model: a passing pre-deployment score marks the start of monitoring, not the end of it. For most enterprises, the production quality checks and evaluation testing that would close that gap still aren't in place.
Perplexity's India revenue rose about 60% after the Airtel offer ended for new users, even as downloads declined.
Security researchers at Anthropic and Switzerland's EPFL have demonstrated that self-propagating payloads can spread from one artificial intelligence (AI) agent to the next through the editable system prompt files that autonomous agent harnesses use to carry state between sessions. The work, released as a preprint on August 10, 2026, tests the technique in a simulated six-agent coding
Reach Capital announced Tuesday an oversubscribed $265 million Fund V.
A new UK trial hopes to reduce the condensation trails from planes, which can trap heat in the Earth's atmosphere.
AI factories are the defining infrastructure of the AI era — where compute transforms energy and data into intelligence that powers every business, industry and country. In the AI economy, compute is revenue. AI factories require a full stack of critical resources: advanced chips, packaging, memory and networking — as well as land, power and […]
Booksellers are reporting mysterious bulk orders. It's thought the books are being used to train AI - and ending up pulped.
