Exploring Continual Learning in AI Agents and Coasean Economics
chalmermagne · x · 2026-09-01
This article delves into the current state and future of continual learning in AI agents. While current agents rely on explicit context injection via complex harnesses and databases, the vision is a 'drop-in remote worker' that learns daily through weight updates, remembering tasks and failure modes over long periods. The piece also explores the economic implications of agent swarms, citing the OpenAI-Hugging Face incident as evidence of improved coherence in multi-agent training involving 1,200 agents.
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