Stanford/Harvard Paper: Agents Fail From Poor Adaptation, Not Low Intelligence
PolarBearby · x · 2026-09-19
A Stanford/Harvard paper explains why most "agentic AI" systems shine in demos but collapse in real use.
The core argument: agents fail not because they lack intelligence but because they can't adapt. Most agents are built to execute plans, not revise them — they assume tools work as expected and goals stay valid, so when reality shifts they keep confidently repeating the wrong move.
Key distinctions:
- Execution: following a plan
- Adaptation: noticing the plan is wrong and changing behavior mid-flight
Today's agents almost only do the first. Adaptation here isn't fine-tuning — agents would need to monitor outcomes and recognize failures, a loop most systems lack entirely.
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