SKYAPI: structural-aware orchestration for LLM-based multi-agent systems
Wang, Phillip
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https://hdl.handle.net/2142/132596
Description
Title
SKYAPI: structural-aware orchestration for LLM-based multi-agent systems
Author(s)
Wang, Phillip
Issue Date
2025-12-09
Director of Research (if dissertation) or Advisor (if thesis)
Lai, Fan
Department of Study
Siebel School Comp & Data Sci
Discipline
Computer Science
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Large Language Model
Multi-agent System
Optimization
Language
eng
Abstract
The proliferation of Large Language Models (LLMs) has catalyzed the development of Multi-Agent Systems (MAS) capable of autonomous reasoning and complex workflow execution. These systems heavily rely on third-party inference APIs, where providers exhibit significant heterogeneity in latency and pricing. However, existing API routing strategies typically optimize requests in isolation, failing to account for the structural dependencies and synchronization barriers inherent in multi-agent collaboration. In this paper, we introduce SkyAPI, a structural-aware routing framework that shifts the optimization paradigm from per-query selection to stage-level orchestration. By decomposing dynamic agent workflows into ordered execution stages, SkyAPI employs a Mixed-Integer Linear Programming (MILP) formulation to explicitly optimize the trade-off between stage makespan and monetary cost. Furthermore, we propose a prefix-aware scheduling mechanism with Time-To-First-Token (TTFT) deferral to maximize KV-cache reuse among collaborative agents. Extensive evaluations on complex benchmarks, including DeepResearch and Gama-Bench, demonstrate that SkyAPI significantly outperforms state-of-the-art baselines, reducing operational costs by up to 3× while satisfying strict latency constraints across diverse model architectures.
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