AGENTS.md is the file that tells any coding agent how to work in your repo. One open AAIF standard, read by Codex, Cursor, Claude Code, and goose. But it is static. Written once, by hand. It drifts as the code moves. Auto-generate it and agents often get worse, following bloated rules and chasing dead paths. The agent fails the same way every run. Nothing feeds those failures back. The text that drives it never learns.
This talk show how to fix that with a layer on top of AGENTS.md that tunes it from the agent's own runs.The loop is small. Run a task. Reflect on the trajectory in plain language. Find what broke. Propose one targeted edit. Validate it against an eval harness before it sticks. Reflect, mutate, validate, select. Borrowed from reflective optimizers like GEPA. No RL. No fine-tuning. Just an agent learning from its mistakes.
The layer is tool-agnostic because AGENTS.md is, and it extends to MCP tool selection. We measure what moved, task success, edits per gain, token cost, regressions on a held-out set. We beat hand-written and auto-generated files. You leave with a layer you drop onto the AGENTS.md you already have.
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Presentation Language: English
Captioning will be available for attendees in 50+ languages through Wordly. See instructions in each room to utilize captioning.
