An Empirical Study of Harness Design for Coding Agents

Run-Ze Fan, Zihao Zhang, Simin Ma et al.1 min readarXivSep 17, 2026

Key Takeaway for Students

Coding harnesses shape how autonomous coding agents translate model capabilities into long-horizon software-engineering performance, yet existing work typically evaluates harnesses as monolithic systems, leaving the effectiveness of individual components unclear. To enable component-level comparisons, we study this question with a lightweight coding harness whose execution loop

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This paper provides crucial insights into the component-level effectiveness of coding harnesses for autonomous agents, which directly impacts the performance and efficiency of AI-driven software development. Understanding these components—planning, action space, and context management—is vital for ML engineers designing and optimizing next-generation coding agents.

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Career Insight

high demand

Why it matters

Skills to learn

Designing and implementing agent-based systems for code generationEvaluating and optimizing context management strategies in LLM applicationsBenchmarking agent performance on software engineering tasks (e.g., SWE-Bench)Performing ablation studies on complex AI system componentsUnderstanding the trade-offs between different planning and action space designs for agents

Interview questions

Describe the key components of a coding harness for an autonomous agent and how each contributes to its overall performance.
Given a scenario where a coding agent struggles with long-horizon tasks, what context management strategies would you investigate, and how would you evaluate their effectiveness?
Design a system for continuously evaluating and improving the performance of a coding agent in a production environment, considering various components like planning, action space, and context management.

Relevant for ~20 months · generated by the enrichment pipeline