Observability-Driven Automatic Evolution of Coding-Agent Harnesses

Agentic Harness Engineering (AHE) is a novel framework that enhances the performance of coding agents by focusing on a system's external engineering architecture, known as the Harness, rather than solely on foundational models. AHE employs three pillars of observability—Component, Experience, and Decision Observability—to optimize and iterate the harness automatically. This approach decouples the harness into distinct components such as system prompts, tools, middleware, and long-term memory, allowing for precise adjustments without affecting unrelated parts. In experiments, AHE demonstrated superior performance by increasing pass rates significantly on tasks compared to human-designed systems and other automatic baselines. The evolved harness also showed remarkable zero-shot transferability across different benchmarks and model families, indicating its robustness and adaptability. The research highlights that the true potential of coding agents lies in enhancing middleware, tool implementations, and memory rather than over-relying on prompt engineering, emphasizing the importance of structured observability in achieving reliable optimizations.

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