Tool-Augmented Language Agents with Iterative Self-Critique for Complex Task Planning
but struggle when confronted with multi-step, complex task planning that requires interaction with external environments. This paper investigates the architecture, implementation, and efficacy of tool-augmented language agents enhanced with iterative self-critique mechanisms. By integrating external application programming interfaces, structured databases, and computational engines, these agents transcend isolated text generation, evolving into active systems capable of executing concrete actions. However, naive tool utilization often results in cascading errors during prolonged execution trajectories. To mitigate this, we introduce an iterative self-critique framework where the agent continuously evaluates its own outputs, identifies logical fallacies or execution failures, and dynamically recalibrates its plan. This research details a comprehensive methodological framework, formalizing the probabilistic decision-making and critique generation processes. Empirical evaluations across simulated complex environments demonstrate that the proposed architecture significantly improves task success rates, minimizes superfluous tool invocations, and enhances error recovery. The findings indicate that integrating reflective cognition paradigms with modular toolsets is essential for deploying autonomous language agents in high-stakes, real-world applications.