As AI agents move from demos to production, error handling becomes the primary engineering challenge. This article examines patterns from teams running agents at scale.
Retry with Exponential Backoff
The simplest pattern — retrying failed tool calls with increasing delays — handles transient failures effectively. OpenClaw's built-in retry decorator makes this a one-line configuration per tool.
Fallback Chains
When a primary tool fails, agents should have alternative tools that achieve the same goal differently. For web scraping, this might mean: direct fetch → headless browser → cached version → ask user. ClawNexus registry tools support declaring fallback alternatives.
Checkpoint Recovery
For long-running agent tasks, periodic checkpointing allows resumption from the last successful step. This pattern is essential for data processing agents that may run for hours.
Human-in-the-Loop Escalation
The most reliable pattern: when automated recovery fails after N attempts, escalate to a human operator with full context. This hybrid approach achieves 99.9% task completion rates in production.
