Unlocking the Future of AI: Understanding Continual Learning at Three Levels
In the evolving landscape of AI, continual learning isn’t just about updating model weights. It occurs at three critical layers: the model, the harness, and the context. Recognizing these layers can transform how you design adaptive AI systems.
Key Layers of Agentic Systems:
- Model: The foundational weights driving AI’s learning.
- Harness: The surrounding code that powers the agent, essential for consistent performance.
- Context: External configurations, including instructions and skills, enhancing the agent’s capabilities.
For example:
- Coding Agent (Claude Code):
- Model: claude-sonnet, etc.
- Harness: Core code tools.
- User Context: CLAUDE.md, /skills.
Continual Learning Categories:
- Model Layer: Commonly discussed through techniques like SFT and RL.
- Harness Layer: Optimization trends through papers like “Meta-Harness.”
- Context Layer: Adaptive settings at agent, user, and organizational levels.
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