Building a Self-Learning Feedback Loop for AI Agents
blaizedsouza · x · 2026-07-10
The post explores how AI agents can self-learn from two signal sources: their own execution trajectories and user corrections when fixing mistakes. It points out that most teams currently only log execution trajectories while missing this crucial user correction feedback.
The author shares an architecture diagram breaking down the self-learning loop into four parts: two types of signal sources, three types of memory (facts, cases, rules), experience storage locations, and how to close the feedback loop.
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