Scaling Enterprise Agent Routing: Degradation, Diagnosis, and Recovery
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Computer Science > Computation and Language
Title:Scaling Enterprise Agent Routing: Degradation, Diagnosis, and Recovery
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 with perfect retrieval, the oracle ceiling drops 10pp). Embedding-based shortlisting recovers +10--11pp F1 at full scale across all three models and two providers. A production annotation study (1,435 human-labeled utterances, three annotators) confirms the recovery on real traffic at +10--17pp despite 10--15pp lower absolute performance.
| Comments: | 10 pages (6 main + 4 appendix), 4 figures, 6 tables |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.17519 [cs.CL] |
| (or arXiv:2606.17519v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.17519
arXiv-issued DOI via DataCite (pending registration)
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