Self-evolving AI agents shouldn't grade their own homework: a 7-step evidence-gated roadmap
MaryamMiradi · x · 2026-09-15
Most 'self-improving' agents have a dangerous loop: the agent proposes a change, tests it, and decides it worked — essentially grading its own homework. Citing the ADMET-EvO paper, the author outlines a stronger architecture where the LLM decides what evidence to acquire next, while deterministic components decide what that evidence actually proves.
The 7-step roadmap highlights:
- Define the system contract: fix the task, metric, boundaries, and allowed evidence up front; specify what the agent may change and what stays protected, so it can't quietly redefine success.
- Diagnose before changing: locate the actual weakness — data scarcity, retrieval failure, or model limitation — before modifying anything.
- Later steps center on evidence-gated verification of each self-modification to avoid confirmation bias.
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