ORACLE: Agentic AI Orchestrator Routing Via Adaptive Verifier Calibration Feedback

תקציר מקורי באנגליתarXiv:2607.22465v3 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). Additionally, due to the verifier placement in the critical path of the loop, serving quality may be affected during multiple concurrent requests routing. To mitigate these issues, we present ORACLE.