Don't Blame the Large Language Model: How Agent Harness Evolution Shapes Coding Agent Quality
Coding agents, autonomous systems that use large language models (LLMs) to resolve software engineering tasks, rely on agent harness: a middleware layer in between a developer and a large language model that orchestrates system prompts, tool execution, context management, and iterative reasoning loops. While these agent harnesses evolve at extreme velocities, no study has examined how this evolution affects agent quality (i.e., effectiveness and efficiency) over time. Practitioners regularly report quality regressions after agent harness updates, yet consistently attribute them to the underlying model rather than the harness itself. In this paper, we address this gap by conducting the fir...
| Authors | Oussama Ben Sghaier, Hao Li, Bram Adams et al. |
|---|---|
| Primary category | cs.SE |
| Published | 4 July 2026 |
| Last updated at source | 20 July 2026 |
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