AI Agent Learning: Governing What Becomes Permanent
Read OriginalThis article explores how AI agents should govern learning that becomes permanent, distinguishing immediate feedback from durable behavior changes. It proposes preserving observations, interpretations, and proposals separately, and qualifying persistent updates through provenance, scope, evaluation, approval, and controlled release. It emphasizes keeping reusable knowledge separate from execution authority and ensuring revocation reaches cached content and active workflows.
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