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Continual Learning in AI Agents Happens Across Model, Harness, and Context Layers

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Learn how AI agents improve over time by optimizing three distinct layers: model weights, harness infrastructure, and external context/memory. The piece breaks down techniques like SFT and RL for model updates, harness optimization via trace analysis and systems like Meta-Harness, and dynamic context learning through persistent memory, tenant-level configuration, and runtime updates. It highlights practical strategies such as offline evaluation loops, agent trace logging, and 'dreaming' workflows to iteratively refine agent performance without retraining models, emphasizing scalable alternatives to weight updates.

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