arXiv:2605.12925v3 Announce Type: replace-cross Abstract: Evaluation of software engineering (SWE) agents is dominated by a binary signal: whether the final patch passes the tests. This outcome-only view treats a principled solution and a chaotic trial-and-error process as equivalent. We show that this equivalence is empirically false. We evaluate 2,614 OpenHands trajectories from eight model backends on 60 SWE-bench Verified tasks. Of these, 47 have enough passing trajectories to construct task-level process references, yielding a 1,815-trajectory evaluation subset. Among passing trajectories
The rapid development and deployment of AI agents requires robust evaluation methods, and this paper highlights a critical flaw in current binary outcome-based assessments.
A strategic reader needs to understand the true capabilities and limitations of AI agents, as current evaluations may overstate their reliability and mask underlying inefficiencies.
The understanding of AI agent performance shifts from a simple pass/fail to a more nuanced process-oriented view, revealing that many 'successful' outcomes are due to 'lucky passes' rather than robust solutions.
- · AI agent evaluation tool developers
- · Companies investing in explainable and robust AI agents
- · Researchers focused on agent process integrity
- · Developers relying solely on binary outcome metrics
- · Companies with low-quality, 'lucky' agent solutions
- · Investors misinterpreting agent capabilities based on current evaluations
Further research and industry adoption of process-based evaluation metrics for AI agents will accelerate.
This will drive the development of more reliable and interpretable AI agents, moving away from brute-force trial and error.
The market for AI agent solutions may segment, with a premium placed on agents demonstrably solving problems robustly rather than merely passing tests serendipitously.
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Read at arXiv cs.AI