Continual-Learning

LangChain published a framework today for thinking about continual learning in AI agents — and it’s one of the clearest mental models for this problem that’s appeared in the wild. This guide takes that framework and turns it into a practical implementation playbook, with code examples for each layer and decision criteria for choosing between them. The three layers, briefly: agents can learn through context (runtime-injected instructions), storage (external memory), or weights (model fine-tuning). Each has different costs, speeds, and durability characteristics. ...

When developers talk about building AI agents that get smarter over time, they usually mean one of two very different things — and they rarely realize the ambiguity. LangChain’s Harrison Chase published a framework today that finally gives the field a shared vocabulary: continual learning for AI agents happens at three distinct layers, and conflating them leads to systems that are overbuilt for simple problems or structurally incapable of solving hard ones. ...