Measuring Cross-Task Behavioral Consistency in Language Model Agents
Agent evaluation relies almost entirely on outcome metrics such as success rate, which capture whether an agent succeeds but not how consistently it behaves. We argue that behavioral consistency across tasks is a distinct and measurable property, and we introduce the Behavioral Consistency Metric (BCM) to quantify it. BCM trains a model to predict task success from behavioral features of agent execution traces, derives a per-trajectory feature-attribution vector, and measures the mean pairwise similarity of these vectors within an agent system. Across roughly 9,000 trajectories from six language model agents on software engineering tasks, our central finding is that cross-task and within-...
| Authors | Amritesh Banerjee, Pranil Raichura |
|---|---|
| Primary category | cs.AI |
| Published | 31 July 2026 |
| Last updated at source | 14 September 2026 |
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