AI Agent Monitoring: How to Observe Autonomous AI Agents in Production

AI agent monitoring — also called LLM observability — is the practice of collecting, analysing, and acting on telemetry data generated by LLM calls and the autonomous agents built on top of them. Think of it as traditional APM, but purpose-built for AI workloads. A modern AI agent is not a static API call. It's a dynamic, multi-step reasoning system that may: Plan and decompose subtasks autonomously Call external tools (web search, code execution, APIs) Retrieve documents via Retrieval-Augmented Generation (RAG) Spawn sub-agents for parallel task execution Loop and self-correct until a goal is satisfied Every one of those steps is a potential point of failure, latency spike, or cost explosion. Just as DevOps engineers would never deploy a microservice without metrics, traces, and logs, MLOps and AI engineers need the same rigour for LLM-powered systems. Why It Matters in Production The jump from a prototype that "works on my machine" to a reliable production AI agent is enormous. Here'