Agentic Harness Engineering
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Most coding-agent harnesses are still tuned by hand or kept alive through brittle trial-and-error self-evolution. This paper introduces Agentic Harness Engineering (AHE), a framework that makes harness evolution observable and falsifiable. AHE separates the system into three layers: components stored as revertible files, experience condensed from millions of trajectory tokens into structured evidence, and decisions written as predictions that get checked against task outcomes. Every edit becomes a contract you can verify or revert.
Three-layer evolution model: Components, experience, and decisions are each first-class artifacts. Components are versioned files, experience is compressed evidence pulled from full trajectory logs, and decisions are explicit hypotheses with expected outcomes. The structure turns black-box harness tuning into an auditable engineering loop.
Pass@1 gains on Terminal-Bench 2: Pass@1 climbs from 69.7% to 77.0% across ten iterations, beating both human-designed Codex-CLI (71.9%) and self-evolving baselines like ACE and TF-GRPO. The framework also uses 12% fewer tokens than the seed harness on SWE-bench-verified.
Cross-model transfer: The evolved harness transfers across model families with +5.1 to +10.1 point gains, suggesting the optimizations are structural rather than overfit to a specific backbone. That is the property you actually want from harness engineering.
Why it matters: Harness work is the largest hidden cost in most agent systems. AHE is the first credible recipe for letting the harness improve itself without drifting into noise, which makes it the most important agent-systems paper of the week.
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