Reinforcement-Driven Prompt Calibration for Autonomous AI Agents

Abstract

This article investigates reinforcement-driven prompt calibration as a strategy for maintaining reliable
behavior in autonomous AI agents. By structuring prompt refinement as a reinforcement learning
optimization task, agents iteratively adjust their prompt formulations based on performance feedback
while preserving semantic intent. Experimental evaluation across stationary and evolving task
environments shows that the method improves behavioral consistency, reduces ambiguity in task
execution, and supports adaptive response patterns without retraining core model weights. The
findings indicate that reinforcement-based prompt calibration provides a scalable framework for long
term autonomous agent reliability in dynamic operational settings.