AgentSWE: Can Coding Agents Build the Agent You Actually Want?
AgentSWE shows coding agents rarely build agents that satisfy full held-out requirements.
AgentSWE is a benchmark of whether coding agents can create, edit, and optimize agents that meet a natural-language requirement. Its 25 tasks let a builder iterate on a few illustrative cases, then score a frozen submission on held-out cases using requirement-derived rubrics or, for optimization, the source benchmark's objective. Evaluations across models and harnesses found that no coding agent came close to delivering the requested behavior. Recurring failures included overfitting to visible cases, hollow runtimes behind complete-looking code, and unsupported claims of completion.
- AgentSWE has 25 tasks covering creation, editing, and optimization.
- Builders train on sample cases; grading uses held-out requirement rubrics.
- No tested coding agent reliably delivered the requested behavior.
- Failures include overfitting, hollow runtimes, and false completion claims.
From the original article
1 min read at huggingface.co
As the production of software passes into the hands of software itself, coding agents are increasingly commissioned to build agents fitted to their users' own workflows. This delegation carries a quiet assumption, that the delivered agent will faithfully serve the requirement it was built for, rather than merely appear to. No existing benchmark tests that assumption.
By Jiahao Wang, Hongjin Qian, Yuyang Hu · huggingface.co
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