ORACLE: Agentic AI Orchestrator Routing Via Adaptive Verifier Calibration Feedback

arXiv:2607.22465v4 Announce Type: replace Abstract: Modern enterprise agent deployments consist of a heterogeneous pool of large language models (LLMs) having diverse capabilities and cost. Existing model routing strategies optimize the quality-cost trade-off, while providing request-level static decisions. More recent solutions address agentic routing as a task-level selection with a serial verifier based router feedback loop. However, their fixed verifier suitable for homogeneous workloads may not generalize to heterogeneous batches of agentic tasks (example: coding, general conversational).

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