Continual learning for AI agents goes beyond updating model weights. It can happen at three distinct layers: the model (weight updates via SFT/RL), the harness (the code and instructions driving the agent, optimizable via meta-harness techniques), and the context (persistent memory/instructions configurable per agent, user, or org). Each layer has different update mechanisms — offline batch jobs or real-time hot-path updates. Traces are the foundational data source powering improvements at all three layers, with LangSmith serving as the collection platform and Deep Agents as a reference harness supporting production-ready context learning.
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Continual learning at the model layerContinual learning at the harness layerContinual learning at the context layerComparisonTraces are the coreSort: