How to Monitor CrewAI Agents in Production

If you're running CrewAI crews in production, you've probably hit this: your cron job exits with code 0, but the crew didn't actually finish its work. The researcher agent got stuck retrying a rate-limited API, the analyst never received input, and nobody noticed until Friday. Multi-agent orchestration frameworks like CrewAI fail differently from traditional services. A crew can fail without crashing. Here's how to catch those failures with heartbeat monitoring — in about 3 lines of code. Why CrewAI crews need dedicated monitoring CrewAI orchestrates multiple agents that call LLMs, use tools, and pass context to each other. Each agent is a potential failure point: Agent hangs: One agent waits indefinitely for an LLM response. The crew stalls, but the process stays alive. Infinite loops: An agent retries a failed tool call endlessly. Your token meter spins, but no useful output appears. Silent quality degradation: The LLM returns garbage, the next agent processes it