Meta-harness optimization loop on Islo: A 200-line POC for automated LLM agent improvement
By
Yossi Eliaz
A second-rack bagel that's nearly first-rack. Tasty stuff.
Summary
A 200-line proof-of-concept demonstrating a meta-harness optimization loop built on Islo sandboxes. The system uses a proposer agent that reads diagnostic logs from prior LLM agent harness candidates, identifies failure modes, and automatically writes improved harnesses. The POC progresses from 0/5 to 5/5 success rate in just four proposer steps, showcasing automated iterative improvement of LLM agent scaffolding.
Key quotes
· 3 pulledA harness is the prompt + tools + scaffolding around an LLM agent.
A meta-harness is the loop that improves the harness automatically: a proposer agent reads the diagnostic logs of prior candidates, spots a failure mode, and writes a better harness.
Goes from 0/5 to 5/5 in four proposer steps.
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