Stanford Course: Building Self-Improving AI Agents Hinges on External Feedback
solyarisoftware · x · 2026-08-08
Stanford has released a 3.5-hour course covering how to build self-improving AI agents from scratch.
The curriculum includes agent basics, multi-step reasoning, and learning from feedback. The author highlights the core insight from the third module: agents cannot improve solely through self-review; external signals (Attempt → Signal → Correction → Better attempt) are essential. This marks a shift from traditional self-evaluation workflows.
Additionally, the tweet references an article on 'Eval Engineering,' discussing how to build automated evaluation gates that allow agents to merge code changes securely without human intervention.
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