LangChain: Continual Learning for AI Agents Happens at Three Distinct Layers — Model, Harness, Context

LangChain Blog·medium signal

LangChain published a framework for understanding how AI agents learn continuously across sessions at three layers: model weights, the harness (code, instructions, tools baked in), and context (external configuration like AGENTS.md that persists between sessions). Their deepagents-cli improved from 52.8% to 66.5% on Terminal Bench 2.0 purely through harness engineering — jumping from outside the top 30 to top 5 without any model changes.