Agent confidence improves by analyzing internal model signals
Do Agents Know When They Succeed? Calibrating Agent Confidence from Internal Representations
Artificial Intelligence
Summary
Knowing when AI helpers succeed is important, especially when they do many steps and interact with tools. The authors studied if the AI's own internal 'thought process' can better predict success than just looking at its output. They created two methods to read these internal signals and tested them on tasks involving coding and commands. Their methods worked better at telling if the AI would succeed without extra slow checks or changes to how the AI is prompted.
What this means in practice
- •For software engineering teams: Improve monitoring of AI code assistants by predicting success from internal signals without extra processing or multiple tries.
- •For customer support ai developers: Increase reliability of AI helpers in interactive systems by calibrating confidence based on how the model internally represents its decisions.
Authors
Priyanka Mary Mammen, Emil Joswin, Srujananjali Medicherla
Abstract
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 an interaction trajectory, and the Action Representation Probe (ARP), which predicts success from representations formed at action decisions. Across three interactive benchmarks (Bash, SQL, Python) and three model families (Qwen14B, Qwen7B, DeepSeek6.7B), our methods consistently outperform surface level generation and sequence-based calibration baselines providing a zero-overhead reliability monitor that requires neither prompt alterations nor multi-sample rollouts.