Reinforced Agent: Inference-Time Feedback for Tool-Calling Agents
Tool-calling agents are evaluated on tool selection, parameter accuracy, and scope recognition, yet LLM trajectory assessments remain inherently post-hoc. Disconnected from the active execution loop, such assessments identify errors that are usually addressed through prompt-tuning or retraining, and fundamentally cannot course-correct the agent in real time. To close this gap, we move evaluation into the execution loop at inference time: a specialized reviewer agent evaluates provisional tool calls prior to execution, shifting the paradigm from post-hoc recovery to proactive evaluation and error mitigation. In practice, this architecture establishes a clear separation of concerns between ...
| Authors | Anh Ta, Junjie Zhu, Shahin Shayandeh |
|---|---|
| Primary category | cs.AI |
| Published | 29 April 2026 |
| Last updated at source | 29 April 2026 |
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