Failure Recovery

Agents fail. Networks time out, models hallucinate, tools error, and edge cases surprise. Failure recovery design determines whether a failure becomes a dead end or a graceful detour.

Failure Types in Multi-Agent Systems

• Agent failure: A single agent crashes, times out, or produces invalid output
• Handoff failure: Context is lost or corrupted during transfer between agents
• Coordination failure: Agents conflict, deadlock, or produce inconsistent results
• Resource failure: External tools, APIs, or data sources are unavailable
• Cascading failure: One agent's failure causes downstream agents to fail

Recovery Strategies

• Retry: Try the same operation again. Works for transient errors (network timeouts, rate limits). Set a retry limit to avoid infinite loops.
• Fallback: Switch to an alternative approach. A different agent, a simpler method, or a cached result.
• Escalation: Pass the problem to a more capable agent or to a human. Used when the failure is beyond the current agent's ability to resolve.
• Graceful degradation: Deliver a partial result rather than nothing. Tell the user what worked and what didn't.
• Compensation: Undo the effects of a partially completed workflow before retrying or escalating.

Designing Recovery Paths

For each point in the workflow where failure is possible:

• What could fail? List the failure modes
• What's the first recovery strategy? Usually retry for transient errors
• What's the fallback? If retry fails, what's the alternative?
• When do you escalate? After how many retries or what type of failure?