Error Handling in AI Agent Systems

Building deterministic workflows with non-deterministic systems (LLMs) is incredibly difficult. When an autonomous AI agent encounters an API failure, hallucinations an invalid JSON structure, or enters an infinite execution loop, how does your system recover?

1. The Self-Correction Loop

Traditional software throws an exception when an API fails. An AI agent, however, can be explicitly programmed to read the exception, understand why it failed, and try an alternative approach.

# Example of passing error context back to the LLM
try:
    result = execute_tool(agent_action)
except ToolExecutionError as e:
    # Do NOT crash. Inject the error back into the prompt.
    correction_prompt = f"Your last tool call failed with error: {str(e)}. Please analyze the error and try a different approach or fix the parameters."
    return agent.replan(correction_prompt)

2. Guardrails and Schema Validation

Never trust the output of an LLM. Use libraries like Pydantic in Python or Zod in TypeScript to strictly validate the data structures returned by the agent. If the schema validation fails, immediately feed the validation error back to the LLM.

3. Timeouts and Iteration Limits

Left unchecked, an LLM agent might enter a recursive loop where it calls a tool, fails, calls the exact same tool with the exact same parameters, and fails again—infinitely burning your API credits.

Always enforce a strict MAX_ITERATIONS limit on your agent's execution loop.

Conclusion

By implementing self-correction loops, strict schema validation, and hard iteration limits, you can build autonomous agents that gracefully handle unexpected errors rather than crashing the entire pipeline.