Scaling Enterprise Agent Routing: Degradation, Diagnosis, and Recovery
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 with perfect retrieval, the oracle ceiling drops 10pp). Embedding-based shortlisting recovers +10--11pp F1 at full scale across all three models...
| Authors | Kellen Gillespie, Robyn Perry |
|---|---|
| Primary category | cs.CL |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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