Common Agent Failure Modes
Understanding typical agent failures helps in building more robust systems and setting appropriate expectations.
Tool Misuse
Agents may call tools incorrectly, with wrong parameters, or at inappropriate times.
Infinite Loops
Agents can get stuck repeating the same actions without making progress.
Goal Drift
Agents may gradually shift focus away from the original task objective.
Over-confidence
Agents may proceed with actions despite uncertainty or incomplete information.
Tool Hallucination
Agents sometimes "invent" tool parameters or even entire tools that don't exist. This usually happens when the tool definition is ambiguous or when the model tries to force a solution.
Looping Issues
Agents can get trapped in repetitive cycles where they perform the same action, receive the same error, and try again without changing strategy.
Cost & Latency
Every step in the agent loop requires a full LLM inference call. Multi-step tasks can quickly become expensive and slow.
The Cost Factor
A simple task requiring 5 steps means 5x the cost and 5x the latency of a standard chat response.
Key Takeaways
- 1Implement safeguards like iteration limits and cost controls
- 2Add human-in-the-loop checkpoints for critical actions
- 3Monitor agent behavior and log all actions for debugging
- 4Design clear success and failure criteria