continual-learning

To keep AI coding agents from forgetting between sessions, you need a continual learning mechanism that stores patterns and conventions in persistent memory. This skill preserves agent knowledge by using a reflective loop to automatically save and apply learnings across future runs.

Continual learning for AI development agents is the process of retaining knowledge across multiple sessions and projects. It uses global and local memory storage to capture tool patterns, project-specific conventions, and common mistakes so agents improve over time without starting fresh.

You set up persistent memory by installing a continual learning hook into your repository. The hook auto-initializes on the first session, integrating with the agent's reflection and persistence flows to automatically store and retrieve learnings locally and globally.

Yes, the agent memory system distinguishes between global and local project conventions. Global memory retains tool patterns and preferences across all projects, while local repo memory captures project-specific conventions and common mistakes unique to that codebase.

To integrate agent memory persistence, you need access to a memory hook system and storage for learnings. The system must also integrate with your agent's existing reflection and persistence flows to automatically capture and apply knowledge during development workflows.

You should use a reflective loop for AI agent memory when your development workflows span multiple sessions or projects. It automatically stores learnings and applies them to improve future runs, which is essential when agents operate across multiple projects and need to retain both global patterns and local conventions.