A company builds a governed context layer specifically to stop its AI agents from confidently giving wrong answers. Once that layer is live, the company is more than twice as likely to report the failure happening — not less. In the past six months, 68% of enterprises have traced a confident but wrong AI agent …
A retrieval-augmented generation (RAG) system is built to answer strictly from the documents it retrieves. But when engineers optimize these AI pipelines end-to-end, the reader module can learn a shortcut: instead of relying on retrieved evidence, it starts answering from its own internal memory — while the system’s overall accuracy keeps climbing. This is the …
One AI module faked 86% of a pipeline’s accuracy gains by feeding another the answers Read More »
Presented by MongoDB Building AI that is accurate, secure, and reliable is a major engineering feat for organizations subject to the compliance obligations that govern healthcare, financial services, and transportation. The challenge of delivering AI-driven products is compounded by the fact that technology in these industries has tended to lag behind other sectors because regulation …
How Heidi built production-ready AI for healthcare at global scale Read More »
Enterprise AI has a new infrastructure problem: companies are accumulating agents faster than they are developing systems to govern them. Gartner estimates that the average global Fortune 500 company will have more than 150,000 AI agents in use by 2028, up from fewer than 15 in 2025. Yet only 13% of organizations believe they currently …
Most teams building retrieval augmented generation (RAG) systems for high stakes classification make the same architectural bet: Route every ambiguous case straight to the language model and trust the retrieved context to sort it out. This works fine in a demo. It falls apart the moment the system has to survive an audit, a regulator, …
Cutting RAG inference costs 6x starts with deciding what never reaches the LLM Read More »
DeepSeek’s V4 Flash has topped model leaderboards and been hailed by developers as a “total monster” since its rollout. But in real-world testing, it completed just 53.8% of a batch of complex agent tasks. Composio ran the model through eight different agent harnesses, including Claude Code, Codex, and OpenCode, on 30 deliberately difficult, multi-step tasks …
DeepSeek’s top-ranked V4 Flash stumbles on real agent tasks as its prices surge Read More »
There is a step in the development process for large language model (LLM)-assisted tooling that most teams skip because it’s tedious, time-consuming, and doesn’t produce results visible to end users: Verifying that what the model is saying is actually correct. Not fluent, not coherent, not topically relevant — correct in the sense of accurately identifying …
Chinese AI startup Z.ai, known internationally for its growing lineup of powerful, largely open source GLM series of language models, today released GLM-5.3 with substantial gains in long-horizon coding and a more consequential — and potentially sensitive — jump in cybersecurity capabilities. Already, GLM-5.3’s cyber capabilities have found a “potentially serious vulnerability in Cursor,” the …
Every Claude model Anthropic tested turned on its own, and no attacker made them do it. Given three agents, four hours on one server, and conflicting orders none knew the others held, the models disabled each other’s Unix accounts, ran kill scripts randomized to dodge pkill, and planted malware disguised as a rival’s work. There …
Google is rolling out Gemini 3.7 Flash, a new version of its workhorse AI model that puts coding, agentic workflows and knowledge work at the center of the upgrade — while temporarily cutting API prices in half. The release arrives just three weeks after the release of Gemini 3.6 Flash, an unusually short turnaround that …
Google’s Gemini 3.7 Flash targets coding and agents with a 50% introductory price cut Read More »