The Reinforced Agent: Hypothetical Time Response of Instrumental Agents
This paper was accepted at the Fifth Workshop on Natural Language Generation, Evaluation, and Metrics at ACL 2026.
Agents calling for tools are evaluated on tool selection, parameter accuracy, and range recognition, however LLM pathway evaluation remains post-hoc in nature. Disconnected from the active performance loop, such tests identify errors that are usually handled by quick tuning or retraining, and basically cannot correct the agent in real time. To close this gap, we move testing to the execution loop at a fixed time: a special reviewer agent checks temporary tool calls before execution, shifting the paradigm from temporary backtracking to active testing and error reduction. Essentially, this diagram establishes a clear division of concern between the primary lethal agent and the secondary update agent. As with any multi-agent system, a reviewer can introduce new errors while correcting others, but no prior work to our knowledge has systematically measured this trade-off. To measure this trade-off, we introduce Helpfulness-Harm metrics: helpfulness measures the percentage of base agent errors that are corrected by feedback; Degradation measures the percentage of correct demeaning responses. These metrics directly inform the reviewer's design by revealing whether a particular model or information provides good overall value. Testing our method on BFCL (single curve) and τ2-Bench (multi-variable conditions), we obtained +5.5% in inconsistency detection and +7.1% in variable functions. Our metrics show that the reviewer's model choice matters: the o3-mini imaging model achieves a 3:1 benefit-to-risk ratio compared to 2.1:1 for the GPT-4o. Automatic acceleration with GEPA gives +1.5–2.8%. Together, these results show the main advantage of separating execution and updating: the updater can be systematically improved through model selection and rapid optimization, without retraining the basic agent.
- ** Work done while at Apple
