Great paper on self-improving agent harnesses.
（bookmark it）
If you maintain a production agent harness， finding every file behind one behavior is often harder than writing the edit.
Harness Handbook builds a three-level map from runtime behaviors to source locations using static analysis and LLM-assisted structuring.
Its BGPD workflow guides coding agents from the system overview to relevant stages， functions， and files， then verifies every candidate against current source.
Across 60 modification requests on Codex and Terminus-2， handbook guidance raised planning win rates from 28.3% to 38.3% and from 26.7% to 45.6%.
Planner token use fell 12.7% and 8.6%.
File- and symbol-level F1 improved in all 24 comparisons against GPT-5.5 and Opus 4.8 reference plans. Complete localization misses fell by as much as 25.9 points.
This is a strong pattern for coding agents that need to evolve large harnesses without losing scattered or rarely executed behavior.
Paper： https://arxiv.org/abs/2607.13285
Learn to build effective AI agents in our academy： https://academy.dair.ai/