Harness Handbook: The Missing Layer for Editing AI Agents

Harness Handbook: The Missing Layer for Editing AI Agents Paper Code Project Demo An AI agent’s capability comes from its harness as much as from the foundation model behind it — the code that builds prompts, manages state, invokes tools, and drives the control loop. As models, APIs, environments, and requirements change, that harness has to be edited continually, and before anyone can edit it, human or coding agent, they first have to find every code location that implements the target behavior. The paper names this the behavior localization problem and argues it is the central bottleneck in evolving a harness. Its two contributions are the Harness Handbook, a behavior-centric representation built automatically from a codebase, and Behavior-Guided Progressive Disclosure (BGPD), a workflow that walks a coding agent from a high-level behavior down to the concrete implementation sites and re-verifies them against the current source. The behavior-to-code gap A harness here is the agent…