arXiv:2605.02411v2 Announce Type: replace-cross Abstract: A semantic gap separates how users describe tasks from how tools are documented. As API ecosystems scale to tens of thousands of endpoints, static retrieval from the initial query alone cannot bridge this gap: the agent's understanding of what it needs evolves during execution, but its tool set does not. We identify this retrieval interface, not planning, as the binding constraint on end-to-end agent performance, and introduce FitText, a training-free framework that makes retrieval dynamic by embedding it directly in the agent's reasoni
The proliferation of complex API ecosystems and the increasing demand for autonomous AI agents necessitate more dynamic and adaptive tooling solutions to bridge the 'semantic gap' in task descriptions.
This development proposes a critical advancement in AI agent capabilities by addressing a core limitation: the static nature of tool retrieval, thereby improving end-to-end performance and expanding the scope of what agents can autonomously achieve.
AI agents will be able to dynamically adapt their tool use during execution, leading to more robust, efficient, and sophisticated autonomous operations rather than relying on predefined, static toolsets.
- · AI agent developers
- · Enterprises adopting AI automation
- · API providers
- · Tasks requiring extensive human oversight for agent tool selection
- · Static tool integration paradigms
Improved performance and broader applicability of AI agents in complex, real-world tasks.
Acceleration of white-collar workflow automation and expansion of AI's autonomous operational footprint.
New competitive landscapes emerge for agent-as-a-service platforms, emphasizing dynamic tooling and ecological adaptation.
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Read at arXiv cs.LG