Making Agent Harnesses Easier to Locate and Modify

Tencent-Hunyuan · hf · 2026-07-16

This article discusses how the real bottleneck of increasingly complex AI agent harnesses isn't just "how to write the code," but rather finding where to make the changes.

The author argues that modern agent capabilities depend on the harness—which assembles prompts, manages state, calls tools, and coordinates execution—as much as the base model. As models, APIs, and environments change, harnesses require frequent modifications. However, production code is often tightly coupled and scattered, making it hard for developers or coding agents to map behavioral requirements to specific code locations.

To address this, they propose two solutions:

Harness Handbook: Uses static analysis and LLM-assisted structuring to automatically synthesize a "behavior-organized" representation from the harness codebase, linking directly to the source code
BGPD (Behavior-Guided Progressive Disclosure): Guides the agent from high-level behaviors down to relevant implementation details, verifying if candidate locations truly correspond to the current source code

On modification tasks across two open-source harnesses, this approach improved behavior localization and edit planning quality while saving planner tokens. The benefits are especially significant in scenarios involving "scattered code," "obscure paths," and "cross-module interactions."

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Making Agent Harnesses Easier to Locate and Modify

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