Unlocking Agentic AI Service Deployment Complexity: Simulation-Guided Strategy Orchestration and Optimization

Abstract

Deploying agentic AI services, such as large language models (LLMs) training and inference, presents significant challenges due to their complex, interdependent design across multiple layers of strategy space(framework, system, transport, network). Addressing diverse user intents with limited cross disciplinary expertise further exacerbates this complexity. To overcome these hurdles, we introduce a novel simulation-guided closed-loop (SGCL) strategy orchestration and optimization framework that is inherently intent-aware. Our approach leverages high-fidelity simulators to evaluate candidate deployment strategies, employs a surrogate-based Bayesian optimization engine to guide the closed-loop process, and incorporates a layer-wise caching mechanism to minimize redundant simulations and reduce evaluation overhead. We demonstrate its efficacy in distributed LLM training. Compared to baselines, our method consistently achieves higher intent satisfaction ratio and significantly boosts orchestration efficiency, notably reducing time-to-target by up to 94.4%. These findings highlight SGCL as a practical and effective solution for reliable, cost- and time efficient deployment of agentic AI services.

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