self-improving

To make an agent learn from corrections automatically, use semantic detection to capture user feedback and errors, then store them in a persistent learning store. This converts everyday feedback into permanent capability without relying on manual updates.

Semantic detection for self-improvement is a mechanism that learns from feedback by understanding meaning rather than matching exact keywords. It captures corrections and errors during interactions to trigger structured learning workflows automatically.

Store reflections and best practices by writing them to a structured learning directory like a workspace folder. Organize feedback into separate files for corrections, errors, reflections, and best-practices to maintain a persistent learning store.

Yes, this self-improving approach works without keyword matching by using semantic detection. It analyzes the meaning of user corrections and errors to capture lessons, ensuring feedback is understood even when exact trigger words are absent.

Lessons get promoted to long-term memory files when they are identified as valuable for future reasoning and task execution. The system promotes these structured learnings from the local learning store to global memory files as appropriate.

Yes, you can use this memory management approach for continuous task improvement. It applies across interactions requiring reasoning and planning, logging observed errors and better approaches to reduce repeated mistakes and accelerate performance.