5 Techniques to Stop AI Agent Hallucinations in Production

<p>Your agent passed all the tests. It's been running in production for three weeks. And it has been quietly wrong the entire time.</p> <p>This is not a hypothetical. Galileo's

<p>You've built the demo. It works. The LLM responds, the tools fire, the output looks great.</p> <p>Then you push it to production — and everything breaks.</p> <p>API calls fail

<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>Vertex AI has been Google Cloud's AI development platform since 2021. On April 22, 2026, at Google Cloud Next in Las Vegas, Google retired it — not with a deprecation notice,

<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>Most developers who build AI agents make the same mistake: they spend weeks designing the orchestration layer, tuning the system prompt, and picking the right model — then hand

<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><strong>TL;DR:</strong> I built the same browser agent twice — once with 500 lines of Python, once with 7 lines of JSON. The second one took 5 minutes. The agent harness layer