做LLM智能体工程化的团队终于有了系统化的架构设计方法论——不再靠直觉拼凑，而是有模式可循、有故障可诊断。建议负责智能体生产部署的架构师和SRE点开，看完能少踩几个坑。
该论文首次将LLM输出与确定性系统之间的边界定义为“随机-确定性边界”（SDB），并视其为生产级智能体运行时的核心原语。作者围绕SDB提出了运行时设计的三个关注点（协调、状态、控制），并给出了六种运行时模式（如层次委派、散聚+Saga、事件驱动序列等），每种模式都追溯了其分布式系统根源并分析了当工作节点变为随机模型时的变化。论文贡献了一套五步模式选择方法论、一个将生产故障映射到模式弱点的诊断流程，以及一种名为“回放发散”的故障模式。通过可靠性分解，论文论证了随着模型方差降低，模式选择和SDB强度对长期可靠性的影响越来越大。
A Methodology for Selecting and Composing Runtime Architecture Patterns for Production LLM Agents
Production LLM agents combine stochastic model outputs with deterministic software systems, yet the boundary between the two is rarely treated as a first-class architectural object. This paper names that boundary the stochastic-deterministic boundary (SDB): a four-part contract among a proposer, verifier, commit step, and reject signal that specifies how an LLM output becomes a system action. We argue that the SDB is the load-bearing primitive of production agent runtimes. Around this primitive, we organize agent runtime design into three concerns: Coordination, State, and Control. We present a catalog of six runtime patterns that compose the SDB differently across conversational, autonomous, and long-horizon agents: hierarchical delegation, scatter-gather plus saga, event-driven sequencing, shared state machine, supervisor plus gate, and human in the loop. For each pattern, we trace its lineage to distributed-systems concepts and identify what changes when the worker is stochastic. The paper contributes a five-step methodology for selecting runtime patterns, a diagnostic procedure that maps production failures to pattern weaknesses, and a failure mode called replay divergence, in which LLM-based consumers of a deterministic event log produce different downstream outputs under model-version or prompt changes. A stylized reliability decomposition separates per-call model variance from architectural momentum, motivating the claim that as model variance decreases, pattern choice and SDB strength become increasingly important levers for long-run reliability. We apply the methodology to five workloads and provide one runnable reference implementation for a 90-day contract-renewal agent.