Self-improving Agent (With Self-Reflection)

You can automate agent self-improvement by recording corrections and applying execution patterns across live sessions. The system persists learning in local files under ~/self-improving and enforces tiered hot/warm/cold memory promotions to continuously refine performance.

Tiered memory storage for self-improving agents is a local file-based system that organizes learned patterns into hot, warm, and cold tiers. It enforces a HOT memory cap and automatically promotes corrections to persist learning across different sessions and namespaces.

To set up local files for persisting AI agent corrections, initialize the self-improving workspace by creating memory and corrections files directly in your home directory under ~/self-improving. This establishes the baseline needed for the automated learning lifecycle.

Yes, the self-improving agent approach works across different memory namespaces. It applies captured execution-improvement patterns across live sessions and multiple namespaces to continuously refine decision quality and reduce repeated mistakes.

A key limitation of using local files for agent memory management is the strict enforcement of a HOT memory cap. This requires tiered promotions to warm and cold storage to manage capacity, and all learning data is confined to the local ~/self-improving directory.

An automated learning lifecycle improves AI behavior by logging corrections, promoting validated patterns through memory tiers, and applying these improvements to future tasks. This reduces repeated mistakes and adapts decision quality to user preferences over time.