arXiv:2606.17519v1 Announce Type: new Abstract: Production LLM assistants route user requests to growing libraries of specialized tools, but how does routing accuracy degrade as the catalog scales? We study single-step routing on a 110-agent, 584-tool catalog from a deployed enterprise productivity assistant, evaluating three frontier models from 10 to 110 agents. Routing F1 on under-specified requests drops 16--23 percentage points across models. An oracle analysis decomposes the degradation into a \emph{retrieval} gap (the model cannot surface the right tool) and a \emph{confusion} gap (even
The rapid expansion and deployment of Large Language Model (LLM) assistants in enterprise settings are pushing the limits of current routing mechanisms, necessitating research into scaling challenges.
This research highlights a critical technical bottleneck for autonomous AI agents in enterprise environments, directly impacting their real-world scalability and effectiveness.
Optimizing AI agent routing becomes a paramount engineering challenge, with implications for the types of tasks and complexity of workflows that can be successfully automated by LLMs.
- · AI platform developers
- · Enterprise productivity software
- · Researchers in AI routing and agent orchestration
- · Inefficient LLM-based agent solutions
- · Enterprises adopting AI without robust routing strategies
Enterprise AI agent deployment will face significant accuracy and scalability hurdles if routing mechanisms are not improved.
The degradation in routing performance could lead to a temporary plateau in the adoption of complex, multi-agent AI systems in business settings.
New architectural paradigms may emerge for enterprise AI, focusing on hierarchical or federated agent systems to manage complexity and improve routing efficiency.
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