Autonomous Tool Selection Algorithms for Large Language Model Driven Agents

Abstract

Efficient and adaptive tool selection mechanisms are required for efficient tool usage, especially in complex environments and with the use of large language model (LLM) based agents. The methods that are used currently rely on a set of heuristics or policies that are static, which leads to sub-optimal task performance, duplication of actions, and high computational cost. In this work, we present a novel tool selection algorithm that is context-aware, predictive, and dynamically selects a tool based on the context and a feedback-driven learning loop. We propose a new algorithm for selecting tools that adapts to the different contexts: predictive, heuristic-based utility assessment, and a feedback-driven learning loop, thus empowering LLM agents to make dynamic tool choices among a large variety of tools. The methodology is formulated as a multiple objective optimization problem, which involves the probability of task completion, the efficiency of the task, and the cost of the task. The algorithm prefilters the candidate set of tools using task constraints, calculates the utility score for every candidate, compares all the candidates' utility scores, selects the best one, and then executes the best tool in the candidate set and keeps monitoring the performance of the selected tool. The results obtained in terms of performance are quite good in comparison with the baseline approaches employed (random selection and static heuristics): the values of Accuracy (92.4 %), Task Completion Rate (89.7 %), Efficiency (0.87), and Computational Cost (22 ms) are very good. They are substantial and stable through the simulated environments and statistically significant in terms of analysis (p < 0.05). The proposed system improves the autonomous LLM agents' scalability, adaptability, and resource efficiency. Dynamic, continuous learning also aids in the improvement of policies about tools over time to ensure consistent performance. Future work will include studying real-time adaptation, integrating multi-agent coordination/reinforcement learning with heterogeneous agent networks. The extensions aim to extend the applicability to real-world applications like smart manufacturing systems, cooperative robots, and intelligent service systems.