Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses
- Published
- Source
- arXiv
- Paper number
- 160
- Field
- Agents / Infrastructure
- arXiv ID
- 2604.25850
Key points
- Manually designing and optimizing coding-agent harnesses, including system prompts, tools, and middleware, is time-consuming and difficult to keep up with the rapid pace of LLM progress.
- Existing automatic optimization methods mostly tune individual harness components and therefore fail to evolve the interacting components together.
- Heterogeneous action spaces, the difficulty of extracting executable signals from long agent trajectories, and complex attribution of edit effects make automated co-evolution of harnesses difficult.
- AHE introduces an evolution agent that keeps the base LLM fixed while continuously optimizing every editable harness component inside a closed loop.
- It uses the component-observability axis of the NexAU harness backbone to separate seven component types into explicit file-level elements, creating a localized action space.
- It implements experience observability with an Agent Debugger that converts raw agent trajectories into a structured corpus of executable evidence, and decision observability with an Evolve Agent that generates evidence-based edits and records them in versioned change manifests.
Paper links
External research summaries. These are not HDATF publications or measured product results.