arXiv:2506.19500v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) increasingly act as function-call agents that invoke external tools to tackle tasks beyond their static knowledge. However, they typically invoke tools one at a time without a global view of task structure. As tools often depend on one another, this leads to error accumulation and poor scalability, particularly when scaling to hundreds or thousands of tools. To address these limitations, we propose NaviAgent, an explicit bilevel architecture that decouples task planning from tool execution through graph-base
The proliferation of LLMs capable of function calls necessitates advanced orchestration tools to manage increasing complexity and scale, addressing current inefficiencies in multi-tool environments.
This development represents a critical step towards more robust and scalable AI agents, enabling them to tackle highly complex tasks with reduced error accumulation and improved planning capabilities.
AI agents move from single-tool invocation to sophisticated, graph-driven planning that integrates a global view of task structure across numerous interdependent tools.
- · AI Agent Developers
- · Enterprise Software
- · Complex Task Automation
- · AI-as-a-Service Platforms
- · Monolithic AI models
- · Manual Integration Solutions
- · Simple Function-Call LLMs
More capable and reliable AI agents will emerge, able to automate multi-step and multi-domain workflows.
The demand for specialized tools and APIs will increase as they become critical components in sophisticated agentic systems, fostering an 'AI API economy'.
This could accelerate the collapse of white-collar workflows, enabling agents to perform tasks previously requiring coordination across multiple human specialists and software applications.
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Read at arXiv cs.LG