How to Monitor and Debug AI Agents in Production

How to Monitor and Debug AI Agents in Production You deployed your AI agent. It worked great in staging. Then production happened. An agent silently started hallucinating responses at 3 AM. Another one entered an infinite retry loop, burning through your token budget in 40 minutes. A third one just… stopped. No errors. No logs. Just silence. If any of this sounds familiar, you're not alone. Monitoring and debugging AI agents is fundamentally different from monitoring traditional software — and most teams learn this the hard way. This guide covers practical patterns for keeping multi-agent systems observable, debuggable, and under control in production. Why Traditional Monitoring Falls Short Traditional application monitoring tracks request latency, error rates, CPU, and memory. These metrics still matter for AI agents, but they miss the things that actually break agent systems: Semantic failures : The agent returned a 200 OK but gave a completely wrong answer Behavioral drift : The age