One bad step, N bad steps: how agent failures cascade

<p>You've heard of DevOps. You've heard of AIOps. You've probably heard of MLOps.</p> <p>But there's a category that doesn't exist yet — and it's the one the AI industry needs

<p>Four agents entered an infinite loop in November 2025. They ran for 11 days. The bill was $47,000. Nobody noticed until it was over.</p> <p>The team was running a market

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1wxfdt9/qwen38_27b_1_x_r9700_262k_context_half_a_million/"> <img alt="Qwen3.8 27B | 1 x R9700: 262K context,

<p>Every AI agent tutorial follows the same script:</p> <ol> <li>Import LangChain</li> <li>Define some tools</li> <li>Call the LLM in a loop</li> <li>Ship it!</li> </ol> <p>And it

<h2> The Problem Nobody Talks About </h2> <p>You've built an agent. You've wired up 100+ tools. You feel good about it.</p> <p>Then it starts hallucinating. Picking the wrong

<p>Most of the failures I see in production AI agents aren't reasoning failures. The model picks the right tool, fills in the right arguments, and makes a perfectly sensible

<p>On March 31, 2026, Anthropic accidentally shipped the entire source code of Claude Code — its flagship AI coding agent — inside a routine npm update. No hack. No reverse

<p>A patch touches zero test files. No assertion changes, no new skips, no fixture edits. The suite goes green on the first run.</p> <p>Then someone opens the job duration graph