ToolChain-CRC: Conformal Risk Control for Agentic AI Under Retrieval and Tool-Use Drift
Modern AI agents retrieve documents, call tools, check intermediate information, and then produce a final answer or action. This creates a risk-control problem that is not visible from the final answer alone. A final response may look acceptable even when the retrieval was weak, a tool output was wrong, or an earlier step was unsupported. We propose ToolChain-CRC, a conformal risk-control method for retrieval-augmented and tool-using agents under drift. The method treats each agent run as a full trajectory of actions, observations, and final output. It builds step-level risk scores, combines them into a trajectory risk score, calibrates an accept-or-intervene rule, and adds an anytime ala...
| Authors | Jeffery Opoku, David Banahene |
|---|---|
| Primary category | stat.ML |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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