Debugging Autonomous Agents: A Survival Guide

If you've built an autonomous agent, you've seen it: the "infinite loop of doom." Your agent gets stuck trying to fix a code error, generating the same wrong fix 50 times in a row until your OpenAI bill hits four figures.

The Three Horsemen of Agent Failure

Building reliable agents is 10% prompt engineering and 90% handling edge cases. Here are the most common failures we see in production:

1. The Loops

Agents often get stuck in a state where they think they are progressing but are actually running in circles. "I see an error. I will fix it." → Fails → "I see an error. I will fix it."

The Fix: Implement a "frustration counter" in your cognitive loop. If an agent tries the same action 3 times with similar negative feedback, force it to stop and ask for human help or change strategies.

2. Tool Hallucinations

LLMs love to invent parameters that don't exist. You define a tool `search_users(query)`, and the LLM calls `search_users(query, limit=10, sort_by="date")`.

The Fix: Strict Pydantic validation on all tool inputs. But more importantly, feed the validation error back to the agent. Don't just crash; let the agent see "Error: unexpected argument 'limit'" so it can self-correct.

3. Context Window Overflow

Long-running agents accumulate history. Eventually, you hit the context limit.

The Fix: Implement memory summarization. When the context gets too full, have the agent (or a secondary model) summarize previous steps into a concise "state of the world" object, and discard the raw message history.

Conclusion

Debugging agents requires a mindset shift. You aren't debugging code; you're debugging a probabilistic decision process. You need tools that give you visibility into the "why," not just the "what."