self-improving-agent

A self-improvement loop for AI agents is set up by running the omnibot_auto_log.sh script, which automatically records non-trivial failures, user corrections, and reusable best practices to enhance agent performance.

Knowledge promotion works by logging errors after tool failures and storing structured learnings, then promoting stable rules into long-term memory for continuous agent improvement and adaptability.

The best way to log agent errors automatically is by executing the omnibot_auto_log.sh script, which captures tool failure data and user corrections in a lightweight loop without interrupting your main task.

Yes, you can store structured workspace learnings from recorded failures and corrections, then promote stable rules into memory for long-term use to enhance agent adaptability.

No external dependencies are required to maintain the self-improvement loop, as the skill operates independently using internal scripts to automate logging and promote knowledge.

You should not use an automated self-improvement loop when your agent tasks require strict stateless execution or when logging non-trivial failures and user corrections introduces unwanted latency.