self-improvement

To log command failures for continuous improvement, you capture error details, exceptions, and unexpected behaviors using structured logging formats. This records API failures and command errors to facilitate later analysis and prevent recurring mistakes in AI agents.

Capturing user corrections refines AI agent workflows by logging feedback and knowledge gaps. When a user corrects an AI's output, the correction is recorded to adjust future responses and promote insights into project memory for continuous improvement.

Yes, you can promote logged learnings directly into project memory files like CLAUDE.md or AGENTS.md. This mechanism elevates captured insights, corrections, and best practices into persistent project memory or extracts them as reusable skills.

The best way to track feature requests and knowledge gaps is to document user-requested capabilities and log missing information systematically. This structures the capture of development insights to enhance overall agent performance and workflow efficiency.

Yes, this continuous improvement approach integrates with agent hooks for proactive error logging. It automatically captures command failures and unexpected behaviors, supporting structured logging formats for various development scenarios without manual intervention.