Enterprise AI deployment is a coordination problem across business units, application and AI teams, testing, platform engineering, infrastructure, security, operations, and data governance. Use-case benchmarks show whether one agent completes one task, but not how changing capabilities, models, runtime mechanisms, capacity, and enterprise data should be owned, changed, admitted, or evidenced together. We present four responsibility objects as shared organizational contracts: Skill (reusable, versioned capability and workflow asset), Harness (runtime compiler and governor), Scaffold (execution/control boundary and NFR owner), and a stack-external data substrate under independent CIO-governed semantics and telemetry. The runtime core is A =, with the data substrate outside that stack. The central contribution is one bounded, falsifiable hypothesis, P1 (cost-aware capability-capacity separability): within a declared operating region, changing activated capability preserves the capacity-response interaction within a preregistered equivalence margin, while changing compatible Scaffold capacity preserves capability semantics up to a non-inferiority margin, and the required controls stay within a declared enforcement budget. Six design conditions become measured obligations whose coverage, violations, uncertainty, cost, and exclusions determine whether P1 is decidable. We propose a cluster-period randomized crossover experiment (balanced order, reset/washout, repeated seeds and failure regimes, cluster-aware uncertainty) with a four-state verdict: supported, falsified, conditional-engineering, or inconclusive. This paper contributes a contract-bounded runtime architecture, a source-preserving data substrate, and a falsifiable measurement protocol. It reports no completed implementation, experiment, dataset, or measured result.
Ya-Xiao Liu, Peng Liu, Yi-Wen Liu et al.· 0 citations
Large Language Model-based Multi-Agent Systems (LLM-MAS) have shown exceptional promise for complex tasks, including retrieval-augmented generation and autonomous data analytics. However, their deployment in resource-constrained industrial environments faces critical challenges, such as unpredictable end-to-end latency, low task success rates, and expensive operational costs. Existing orchestration strategies for LLM-MAS mainly rely on static heuristics, implicit LLM-based routing, or reinforcement learning, which suffer from brittleness under workload drift, prohibitive online exploration costs, and inherent governance risks. To address these issues, we propose G-STAR, a general graph-based scheduling framework that formalizes complex MAS pipelines as attributed Directed Acyclic Graphs (DAGs). Specifically, G-STAR decouples its learning pipeline into three critical stages to ensure both system stability and execution efficiency. First, we build a data layer that logs fine-grained signals for agent node (e.g., execution latency, token I/O, model IDs, resource metrics) and task-grounded outcomes (e.g., accuracy, cost, SLA), yielding a trace-driven dataset. Second, we train an offline GNN model to predict optimal dynamic routing decisions, including agent activation, concurrency-constrained Top-K selection, and execution ordering, conditioned on the evolving graph state. Finally, a lightweight dispatcher deploys the pre-trained GNN model for single-forward-pass routing with negligible computational overhead. Extensive experiments on two public benchmarks and our real-world production workload confirm G-STAR's superiority over static and LLM-driven baselines. It maintains competitive task quality while reducing latency variability and consistently improving end-to-end latency. Furthermore, we develop an industry-grade orchestration stack with asynchronous execution, resilient serving, and audit-friendly artifacts, offering a practical solution for optimizing web-scale deployments of complex MAS pipelines.
Jiabao Song, Yunsheng Xia, Beibei Kong et al.· Proceedings of the 32nd ACM...· 0 citations