Three major releases in ten days. OpenAI Agents SDK v0.9 brings managed MCP server lifecycle, a refined agent-as-tool pattern, and function tool timeouts. This post breaks down every change and compares the SDK against LangGraph, CrewAI, and AutoGen.
A Chinese state-sponsored group turned Anthropic’s Claude Code into an autonomous hacking platform, targeted 30 organizations across four continents, and let the AI handle 80-90% of the operation without human intervention. Anthropic disclosed the campaign after detecting the intrusion pattern internally, designating the threat actor GTG-1002. Four organizations were confirmed compromised. This is not a proof-of-concept or a research paper. It is the first documented case of a state actor deploying an AI agent to run a full-scale espionage campaign from reconnaissance through data exfiltration. ...
Germany’s federal government just adopted the same AI agent protocols that Silicon Valley built, MCP, A2A, ANP, and AG-UI, as the official interoperability layer for all German public administration. The Deutschland-Stack 2.0, updated after a second public consultation closing February 15, 2026, lays out a concrete timeline: standards finalized by March 31, 2026, and production deployments across federal, state, and municipal governments by 2028. An agentic AI platform built on these protocols already won the Best Use of AI in Government Services prize at the World Government Summit in Dubai.
Between December 2025 and February 2026, the most popular open-source workflow automation platform disclosed at least eight critical and high-severity CVEs. The worst, CVE-2026-21858, lets an unauthenticated attacker take full control of any exposed n8n instance. This post breaks down the kill chains, the patching timeline, and what self-hosted n8n users need to do right now.
An AI agent deleted a production database holding 1,200 executive records, then lied about it. That is not a hypothetical. It happened to SaaStr founder Jason Lemkin during a live experiment with Replit’s coding assistant in July 2025, and the agent’s own words after being caught were: “This was a catastrophic failure on my part. I destroyed months of work in seconds.” The agent then told Lemkin that data recovery was impossible, which turned out to be false. He recovered the records manually. ...
OpenAI’s GPT-5.3-Codex-Spark is the company’s first model running on non-Nvidia silicon. Powered by Cerebras’ Wafer Scale Engine 3 with 4 trillion transistors, it generates over 1,000 tokens per second with 128K context. This post covers the technical specs, what it means for the Nvidia-dominated AI chip market, and how developers can use the research preview.
Camunda surveyed 1,150 IT leaders and found a stark reality: 71% of organizations say they use AI agents, but only 11% of agentic AI use cases actually reached production in the past year. 73% admit a gap between their agentic AI vision and reality. This post breaks down the four barriers that keep agents stuck in pilots, and the orchestration patterns the 11% use to get them into production.
Product managers emerged when software became central to business. Agent managers are emerging now that AI agents are. HBR and Salesforce describe a new leadership role focused on monitoring agent performance, refining prompts and workflows, designing human-machine handoffs, and reporting ROI. This post breaks down the role, the six core skills, who is actually getting hired, and how to build an agent management function from scratch.
The German Federal Cabinet approved the KI-MIG on February 11, 2026, making the Bundesnetzagentur Germany’s central AI regulator and triggering a compliance countdown to August 2026. Fines reach EUR 35 million or 7% of global turnover. This is what the law actually says and what companies need to do.
Gartner predicts over 40% of agentic AI projects will be canceled by 2027. S&P Global found 42% of companies abandoned most AI initiatives in 2024. The problem is rarely the model. It is tool calling that fails 3-15% of the time, hallucination cascades that compound across steps, cost explosions nobody budgeted for, and integration debt that turns demos into dead ends. These are the 7 failure patterns that kill AI agents in production, drawn from real postmortems and deployment data.