continual-learning

To make an AI agent remember tool outcomes across sessions, you need a continual learning loop that captures experiences, reflects on failures, and persists learnings in memory files or databases for future action.

Agent memory scoping for coding conventions works by separating global learnings for cross-project tool patterns from local learnings for repo-specific conventions, ensuring persistent context is applied correctly to future actions.

The best way to stop AI coding agents from repeating mistakes is implementing a capture and reflect mechanism that turns observed tool failures into stored learnings, compounding improvements over repeated sessions.

Yes, this continual learning approach supports deterministic hook-based automatic capture, alongside agent-native database persistence and human-maintained memory files under version control for storing learnings.

Yes, you can persist coding conventions and agent reflections in a database using agent-native persistence, or alternatively maintain them in human-maintained memory files under version control.

You need to compact agent memory for continual learning when stored learnings grow too large, ensuring the persistence mechanism maintains efficient global and local scoping for future agent actions.