Dynamic AI Tool Orchestration Framework for Enterprise Scale AI Agents

Abstract

Introduction: LLMs have enabled the creation of AI agents that can perform complex tasks that require access to different tools and APIs. In enterprise applications, AI agents are invoked to interact with an ecosystem of hundreds of tools specifically designed to query databases, perform financial validation, scan documents, and automate tasks. Choosing the right tool for the task is an architectural consideration that affects the system's scalability, latency, and correctness.

Objectives: This paper proposes the Dynamic AI Tool Orchestration Framework (DATOF), a scalable smart tool selection framework for enterprise AI agents, designed to enable automatic tool discovery and selection across large, heterogeneous tool registries with minimal latency and execution errors.

Methods: DATOF is built on a modular structure comprising intent classification, semantic indexing of tools, tool capability metadata, and an adaptive multi-criteria orchestration engine that ranks and selects candidate tools for each user query.

Results: In simulated enterprise workloads, DATOF achieves a 32% increase in tool allocation accuracy and a 28% decrease in decision time relative to static baselines, while maintaining stable performance as the tool registry scales from 50 to 500 tools.

Conclusions: DATOF is a reliable and scalable approach to building AI agents able to meet user needs in complex enterprise software ecosystems.