Do Agents Know When They Succeed? Calibrating Agent Confidence from Internal Representations
As agentic systems getting adopted rapidly in safety critical applications, it is vital to measure the confidence associated with the agentic actions. In comparison to the traditional machine learning systems, agentic workflows have complex failure modes with planning, tool invocation and dynamic environment interactions. In this paper, we investigate whether model's internal representations provide stronger signals of eventual task success in multi-turn agentic setups. We introduce two complementary methods: Latent Trajectory Dynamics (LTD), which summarizes changes in residual-stream representations across an interaction trajectory, and the Action Representation Probe (ARP), which predi...
| Authors | Priyanka Mary Mammen, Emil Joswin, Srujananjali Medicherla |
|---|---|
| Primary category | cs.AI |
| Published | 8 September 2026 |
| Last updated at source | 12 October 2026 |
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