How do AI agents handle error recovery and self-correction?

AI agent error recovery involves detecting and correcting mistakes during task execution. Detection mechanisms include checking tool call return values for errors, validating outputs against expected schemas, comparing results against sanity checks, and monitoring for logical inconsistencies. Self-correction strategies include retry with modified parameters when a tool call fails, alternative approach selection when the primary method does not work, backtracking to an earlier state and trying a different path, asking clarifying questions when the agent detects ambiguity, and graceful degradation providing a partial answer rather than failing completely. Implementing robust error recovery requires comprehensive error handling in tool implementations, providing the agent with error context in the prompt, training or prompting agents to reason about failures and alternative approaches, setting maximum retry limits to prevent infinite loops, and maintaining execution logs for post-hoc analysis. Reflection-based approaches have the agent explicitly evaluate its own output quality and revise if needed.