Can LLMs Engineer Their Own Agent Harness? ByteDance Seed’s HarnessDev Says Only 34 of 64 Changes Generalize

Researchers from ByteDance Seed and partner institutions have introduced HarnessDev, a benchmark designed to evaluate how effectively AI models can engineer their own testing frameworks. By assessing the quality of the executable harnesses generated by models rather than just their output, the study found that only 34 of 64 autonomous modifications successfully generalized across tasks. This tool highlights the current limitations of LLMs in reliably automating the software engineering process required for complex agent workflows.