Llm Ops

Your AI agent is stuck in a loop. It tried the same API call three times, got the same 503, and it’s about to try a fourth. The log looks like a broken record. This is a deterministic agent loop failure — and it’s the single most common reason production agent deployments fail in 2026. I’ve been running autonomous agents in production for the past year, and loop failures are the problem that keeps coming up. Not model quality, not prompt engineering — agents that get stuck repeating the same failing action until they burn through their token budget or hit a hard timeout. The frameworks that work in demos break in production because they treat the LLM as a reliable component. It isn’t. Here’s what I’ve learned about why loops happen and how to actually fix them. ...

The AI agent API cost horror story in 2026 is not a single expensive prompt. It is usually a loop: an agent retries a tool, hands off to another agent, grows context, and keeps spending after dashboards have already warned you. The fix is hard runtime limits, not better vibes around prompt engineering. Why Are AI Agent API Cost Horror Stories Surging In 2026? AI agent costs are surging because the unit of failure changed. A chatbot request fails once. An agent request can fail for hours. ...