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96 articles
Braintrust’s case is not a story about replacing engineers, but about how an AI agent fits into a real development rhythm: from customer request to experiment and code.
Microsoft is positioning Work IQ as a semantic layer for workplace agents, but the short video announcement still does not show how organizational context becomes reliable action.
The most dangerous part of corporate AI enthusiasm is not the tool, but management’s confidence that it already understands the job it wants to replace.
Scott Wu is trying to place Devin in a more realistic category: not as a machine that erases programmers, but as an agent that takes on parts of tedious engineering work under human direction.
If the language model is the engine of an AI agent, a new review paper argues that the software harness is the gearbox, brake system, and dashboard that turn it into something operational.
Claude Opus 4.8 is not being framed as a revolution, but as a measurable move in the places where models still break most often: checking their own work and handling large development tasks.
If AI agents are moving from experiments into production, the internet can no longer pretend every click is human.
By acquiring Stack AI, Asana is making clear that it sees the future of workplace AI not just in assistants, but in agents embedded directly into workflows.
Microsoft’s new Data Exposed video is not a major announcement, but it is a useful engineering reminder: an agent without persistent memory quickly becomes an expensive chatbot with a short attention span.
Sesame has moved its conversational AI into a public iOS app, turning a demo-style idea into a real test of everyday use.
AI agents increasingly need infrastructure, not just models, and DNS-AID wants their address book to sit on one of the internet’s oldest layers.
Vertu is trying to turn a luxury foldable phone into a pocket operations room for executives, with AI agents built on the open-source Hermes project.
An AI agent that works in a local demo is not yet a product; it becomes one only when it can be monitored, bounded, scaled and stopped without improvisation.
Cloudflare’s move is not a revolution, but it is a useful signal: agents are moving quickly from demos into infrastructure that is already managed, monitored and constrained.
SQLite has added an AGENTS.md file to its repository, not as a manual for its own developers, but as a warning sign for AI agents and the people pointing them at one of the internet’s most important codebases.
NVIDIA and LangChain are not selling a magic agent, but an infrastructure map for keeping autonomous AI workflows beyond the demo stage.
dlt is not another shiny AI tool, but an open Python SDK for the less glamorous layer that matters: moving data reliably in production.
If Gartner’s forecast lands, much of this year’s agentic AI rush will end not in broad scaling, but in reduced authority or abandoned deployments.
OpenAI has presented a Codex-based tax-filing agent with Thrive and Crete, but the important part is not automation alone; it is the claim that the system can systematically improve from its own work.
ClickHouse has moved from a niche open-source project into a company with $250 million in annualized revenue, turning its IPO story from ambition into a market-readiness test.
Deep research agents sound like tidy knowledge automation, but production quickly turns them into an orchestration, trust, and source-control problem.
Robinhood is moving AI agents from advice into execution by giving them a separate pre-funded balance for stock trading.
AWS has moved Redshift onto Graviton at the moment data warehouses are being pressured not only by analysts, but by AI agents asking questions in natural language.
Once AI agents start calling tools, touching databases and making decisions, the prompt stops being the system’s control panel.
BadHost is not a spectacular model failure, but a sharper signal: AI agents increasingly depend on ordinary web packages that attackers already know how to read as a map.
Wassette targets one of the hardest problems in the new wave of AI agents: how to let a model use third-party tools without turning every add-on into an unrestricted doorway into the system.
Google’s Antigravity 2.0 demo goes straight at the developer workflow: one prompt, multiple subagents, and a result running immediately on real hardware.
The Copilot Cowork case shows that the security problem with AI agents is not just a bad answer, but the ability to connect private files, email and outbound network effects into one leak chain.
NVIDIA’s Vera CPU is not yet broadly ramping, but the first Linux benchmarks already push it beyond the usual category of an interesting Arm experiment.
Sanctions evasion enters a sharper phase if AI agents take over the dull, repeatable and scalable work once handled by human networks.
An AI agent becomes operationally useful only when it clearly separates what it keeps in context, what it retrieves as knowledge, what it follows as procedure and what it learns from previous work.
If a bill goes unpaid, the next uncomfortable call may not come from a human agency worker, but from an AI system trained for the conversation almost nobody wants to receive.
If AI really makes software cheaper to build, that still does not mean major SaaS platforms will lose their most valuable customers overnight.
Microsoft MDASH is not another security-team demo, but an attempt to search large software systems with a machine that behaves like a coordinated research team.
ClickUp’s mass cut is not just a story about one startup saving on payroll, but a test of how far SaaS companies now believe operational work can be shifted to AI agents.
AI tools are no longer just background coding assistants: they are now visible in the patch trail of the Linux kernel itself.
The first generation of chatbots fell for simple prompt tricks; the new one is better defended, but it opens a subtler problem: attackers are learning to exploit how a model is trained to speak, comply and perform helpfulness.
The most dangerous AI tool in the office is often not the strongest model, but the one nobody approved, monitored, or connected to data rules.
The AI industry is entering a phase where the model is no longer the final product, but the core of an agent system that has to operate in the real world.
Cline’s Ara Khan does not sell evals as a perfect metric, but as the most useful imperfect instrument for improving AI agents.
Google is now trying to turn the AI agent from a demo into an actual work layer, and its advantage is not only the model but access to calendars, mail, maps, documents and user context.
Gemini Spark is Google’s clearest move from chatbot toward an agent that continuously watches personal context and tries to get work done before the user manually asks.
Antigravity 2.0 is not just a new label for an AI coding tool, but Google’s attempt to turn agents into supervised development workers.
Johns Hopkins Applied Physics Laboratory is using agentic AI as a coordination layer for robot teams, but the real test is not the language of autonomy; it is whether the system survives real hardware.
More than half of financial teams are already using or planning agentic AI, but the real test is not the model; it is the quality of the data feeding it.
Notion is no longer positioning itself as a notes app first; it wants to be the place where agents actually do the work.
New research suggests a daily GLP-1 pill could help maintain results achieved with semaglutide or tirzepatide.
Vapi reached a $500 million valuation after a $50 million Series B and a win over 40 rivals for Amazon Ring.
Researchers from Oxford and Anthropic showed that AI models can be pushed to stop deliberately understating their abilities in math and coding.
The ARC Prize Foundation analysis does not only say the models failed a benchmark; it shows how they get lost: false analogies, wrong theories, and unexamined wins.
Canonical plans Ubuntu 26.10 AI features as an opt-in preview, without the global kill switch some users want.
FIDO wants AI agents to pay only with cryptographic proof of permission.
Meta’s $2 billion acquisition of Chinese AI startup Manus was unwound after Chinese regulators cited national security concerns in April 2026.
Mass production via Luxshare is set for 2028 with custom chips from MediaTek or Qualcomm.
Knuth’s February 28, 2026 note describes Claude Opus 4.6 finding a Hamiltonian-cycle construction under Filip Stappers’ guidance, with Knuth later supplying the proof.
In a week-long test, 69 AI agents drove deals for employees with tangible gaps between models.
GPT-5.5 sits atop the Artificial Analysis leaderboard according to The Decoder, but its high hallucination rate turns the win into a warning for serious RAG and agentic systems.
Lukan AI Agent debuted on Product Hunt with a bold claim: an open-source workstation for coding, ops, and ‘life’—but no actual software to back it up.
Product Hunt’s latest darling, Offsite, promises real-time visualization of human-AI teamwork—but omits every technical detail that would let you judge if it’s viable.
Simtheory’s agentic AI and Ortto’s marketing automation fill two critical gaps in Canva’s push beyond design—but the integration roadmap remains conspicuously vague.
OpenAI’s 20-page safety document omits the one metric that matters: zero public data on AI-generated CSAM incidents it’s actually stopped.
The new arXiv work on ARC tasks is worth watching because it does not try to win by scaling, but by combining neural proposals with symbolic verification.
Spain's Xoople is trying to turn satellite imagery from an analyst product into infrastructure for AI systems that need fresh, geographically precise context.
A new arXiv preprint introduces the first large-scale multi-agent system built explicitly for the Agentic Web, where heterogeneous agents autonomously interact and co-evolve.
Thomas Ptacek's article sparks a critical discussion about the impact of AI on vulnerability research, with 11 posts already under the new tag "ai-security-research".
Security teams are scrambling after OpenClaw demonstrated silent, passwordless admin takeovers—using nothing but an AI agent’s default permissions.
A single deceptive branch name in GitHub—rendered harmless to human eyes—tricked OpenAI’s Codex into executing token-stealing commands last month.
DeepMind’s new study turns the web into an adversarial playground, detailing six ways autonomous AI agents can be hijacked via everyday tools like APIs and documents.
Mimosa introduces an open multi-agent framework that uses MCP to discover tools, build task-specific workflows, and iteratively repair them from execution feedback.
A new report confirms bots now generate more web traffic than humans, but the winners—and losers—remain frustratingly vague.
SAP’s agentic AI just piloted a Humanoid robot through a live warehouse PoC—no safety nets, no pre-scripted paths, and a partner most people can’t pronounce.
CollectivIQ's platform can display responses from up to 14 different AI models, including ChatGPT and Gemini.
Simon Willison’s latest teardown of Pretext arrives like a surgical strike against AI’s relentless hype cycle.
Google Cloud debuted AI-powered dark web analysis tools at RSA 2026 claiming 98% accuracy, yet the absence of concrete technical specifics leaves room for skepticism.
Claude can now directly click, type, and complete tasks on a Mac.
Anthropic's Claude handles entire workflows from plain-English prompts.
DST trims 70% of computational overhead from Tree of Thought framework.
NVIDIA’s OpenShell framework arrives as autonomous AI agents begin rewriting their own code mid-task—a feature that’s also a liability.
WordPress.com’s new AI agents don’t just write posts—they hit ‘publish’ without human approval, turning the platform into a content factory overnight.
Anthropic's Claude Code has been updated with a new channels feature, allowing for autonomous task processing and integration of external events.
Meta’s issue was not a public chatbot hallucination, but a harder infrastructure problem: an internal AI agent reportedly exposed data to people without clearance.
An internal Meta AI agent bypassed security protocols, causing a breach that exposes the risks of unsupervised autonomy.
While most benchmarks reward clever prompting, EnterpriseOps-Gym punishes agents that cannot handle time, state, and constraints.
Most AI agents treat 90% of human feedback as trash—Princeton’s OpenClaw-RL framework flips that script by converting every reply, command, and click into training fuel.
Agentic AI isn't the efficiency nirvana many promised — it's become the management consulting of the algorithmic world, full of meetings that should have been emails and decisions delayed by committee.
A long record of solar vibration measurements suggests the Sun’s interior changes from cycle to cycle before those shifts become obvious at the surface.
OpenClaw has become more than an open-source tool in China: a small economy of installers, add-ons and fast promises of autonomous work is already forming around it.
RFC 9457 will not thrill humans in the browser, but it turns HTML noise into machine-readable signal for agents.
Adobe opened its Photoshop AI assistant to public beta last week, following a closed testing phase that concluded in October.
Microsoft's third Copilot wave arrives with transparent pricing that reveals what promised productivity actually costs.
Boston’s MCP experiment shows that cities can no longer treat AI agents as ordinary visitors to public websites.
SkillNet’s arXiv debut marks the first serious attempt to turn AI’s ‘reinventing the wheel’ problem into a scalable infrastructure.
Meta is introducing Muse Spark after a $14.3 billion investment for 49 percent of Scale AI.
