Error Handling & Recovery
Error taxonomy, retry strategies, graceful degradation, and self-correction.
Error Categories in Agent Systems
A taxonomy of the four major error categories in AI agent systems — tool execution failures, LLM reasoning errors, state corruption, and environmental errors — along with their frequency, severity, and appropriate handling strategies.
Graceful Degradation
Strategies for maintaining useful agent behavior when one or more skills are unavailable, including fallback chains, capability degradation matrices, and user notification patterns.
Retry Strategies and Backoff
A guide to when and how to retry failed operations in agent systems, covering exponential backoff with jitter, idempotency considerations, and the critical distinction between retryable and non-retryable errors.
Self-Correction and Reflection
Techniques for building agents that detect their own mistakes and fix them, including output validation, reflection prompts, the Reflexion pattern, and post-tool-call verification — typically improving task success rates by 10–25%.