continuous-learning-agent

Continuous Learning Agent

A meta-skill that enables AI agents to learn from experience and improve over time by separating journaled memory from policy changes that alter future behavior.

Core Concept

Traditional agents reset completely between sessions. This skill treats memory and learning as related but distinct operations:

• Journal / memory records what happened, what was tried, and what evidence exists.
• Learning / policy changes what the agent will do next time for a recognizable event class.

Do not call a session log, decision journal, or context note "learning" unless it produces a policy delta, threshold revision, banned move, or acquired pattern that changes future behavior.

Learning Mechanisms

Every learning loop has two layers:

1. Journal layer: episodic or semantic records used for auditability.
2. Policy layer: compact behavioral deltas used to improve performance on future tasks.

The journal layer is optional when an existing memory system already covers it. The policy layer is mandatory for this skill.

1. Error Pattern Recognition

After each error, first document the event if no existing memory system already captures it:

## Error Log Entry

**Date**: 2026-01-30
**Context**: Implementing user authentication
**Error**: TypeError: Cannot read property 'id' of undefined
**Root Cause**: Missing null check before accessing user object
**Fix**: Added optional chaining: user?.id
**Pattern**: Always validate object existence before property access
**Prevention**: Add TypeScript strict null checks

Then extract the policy delta:

## Policy Delta: [Short Title]

**Date**: 2026-01-30
**Event Class**: Accessing nested properties on possibly absent objects
**Prior Policy**: Read nested properties directly after optimistic object construction.
**Failure Mode**: Undefined objects caused runtime TypeErrors.
**Revised Policy**: Validate object existence or use typed optional access before nested reads.
**Trigger**: Any code path receiving user, API, database, or tool-returned objects.
**Propagation Target**: Project AGENTS.md, test helper, lint rule, or skill source.
**Verification**: Add or run a test that fails under the prior policy and passes under the revised policy.