CyberGraft: Evolving the Harness Design Space through High-Leverage Mechanism Mining

Automatic harness evolution improves agents without model retraining through trajectory analysis and candidate evaluation. Yet even with accurate root-cause diagnosis, finding effective revisions in a vast design space requires extensive trial and error. Fixed seed harnesses adapt poorly to heterogeneous tasks, unconstrained edits or rigid module recombination can produce low-value candidates, and useful external mechanisms remain difficult to discover and consolidate for reuse. We propose CyberGraft, a framework that continually evolves the harness design space by constructing, reusing, and curating high-leverage mechanism assets. Each asset combines a carefully engineered, reusable mechanism package with a scoped, quantitatively grounded benefit–risk profile. Diagnosis-guided discovery seeks implementations addressing observed needs, while performance-guided discovery investigates strong external agents for alternatives beyond the current diagnosis. Contribution analysis identifies promising core mechanisms, and dependency-guided reconstruction refines their extraction boundaries through validation. Separate comparisons assess source contribution, reconstruction fidelity, and conditional transfer utility. Evidence-guided retrieval supports task-conditioned seed assembly and subsequent candidate generation. Accepted and rejected trials refine packages and profiles, while unmet needs drive further discovery, accumulating both implementations and evidence for future optimization. On Evo-Bench across Search, Office, and General tasks, CyberGraft achieves an Overall score of 53.6 and an AnytimeVal of 54.8, exceeding native evolution by 7.3 and 4.7 points under matched online resource limits with a prebuilt library. Ablations show that continual discovery and adaptive boundaries improve final performance, contribution screening reduces online cost by 27.6% at the same reported performance, and profile-guided reuse raises gate acceptance by 19.8 percentage points.