Robustness Analysis of Agentic AI to Inconsistent and Incomplete Tool Responses
Tool-using agents increasingly rely on external tools to complete multi-step tasks, but tool returns can fail in different ways and require different recovery actions. Existing robustness studies often use uncertainty-based measures to detect when an agent becomes unreliable. These measures can reveal that something has gone wrong, but they do not directly identify the type of tool failure or the appropriate response. We address this limitation by analyzing tool failures at the moment a return enters the agent context. Our approach combines two complementary signals. The first compares the likelihood of the returned content under the tool schema and under the full trajectory prefix. The s...
| Authors | Jiachen Xu, Torben Bach Pedersen, Zhongming Yao et al. |
|---|---|
| Primary category | cs.AI |
| Published | 24 August 2026 |
| Last updated at source | 30 August 2026 |
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