Beyond the Black Box: Interpretability of Agentic AI Tool Use
AI agents are promising for high-stakes enterprise workflows, but dependable deployment remains limited because these tool-use decisions are difficult to diagnose and control. Agents may skip required tool calls, invoke tools unnecessarily, or take actions whose consequences become visible only after execution. Existing observability methods are external: prompts reveal correlations, evaluations score outputs, and logs arrive only after the model has already acted. In long-horizon settings, these failures are costly because an early tool mistake can alter the rest of the execution trajectory, increase token consumption, and create downstream safety and security risk. We introduce a mechan...
| Authors | Hariom Tatsat, Ariye Shater |
|---|---|
| Primary category | cs.AI |
| Published | 7 May 2026 |
| Last updated at source | 5 July 2026 |
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