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Michele Catasta on X: "Continual Learning for Agents" / X

x.com · 2,350 words · saved by 1 readers

https://t.co/3BAeQ795hm

Everyone talks about Continual Learning as if it means one thing only: updating model weights. But there's an inconvenient truth about the agent ecosystem — the vast majority of agents in production today leverage closed frontier models. When you don't own the weights, you certainly can't fine-tune them. For most agent builders, weight-level continual learning is off the table, especially when working at the very frontier of capabilities (think Fable 5 or GPT 5.6). That doesn't mean agents can't learn. Agentic systems can improve at three layers — model, harness, and context [0] — and the…

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