Harness Handbook: Making Evolving Agent Harnesses Readable, Navigable, and Editable

Jul 16, 2026

A new preprint introduces the Harness Handbook, an automatically generated, behavior-centric representation of agent harness codebases created through static analysis and LLM-assisted structuring. The paper also presents Behavior-Guided Progressive Disclosure (BGPD), a method to help developers and coding agents efficiently locate code implementing specific behaviors. Experiments on two open-source harnesses demonstrate that Handbook-Assisted planning improves behavior localization and edit-plan quality while reducing token usage, especially for complex or distributed code changes.

Why it matters: This work addresses a major challenge in evolving complex AI agent systems by automating the mapping from high-level behaviors to relevant code locations, potentially streamlining development and maintenance.

Full story at: arXiv AI/ML ↗