多智能体系统在复杂任务中常因推理和协调瓶颈而失效，APWA通过并行分解解决了这一痛点，做分布式AI系统或智能体编排的开发者值得关注其设计思路。
论文提出APWA（Agent-Parallel Workload Architecture），一种分布式多智能体系统架构，旨在高效处理高度可并行化的智能体工作负载。APWA通过将工作流分解为无干扰的子问题，并利用独立资源并行处理，无需跨通信。该架构支持异构数据和多种并行模式，适用于广泛领域。实验表明，APWA能动态分解复杂查询为可并行工作流，并在先前系统完全失败的大规模任务上实现扩展。
APWA: A Distributed Architecture for Parallelizable Agentic Workflows
Autonomous multi-agent systems based on large language models (LLMs) have demonstrated remarkable abilities in independently solving complex tasks in a wide breadth of application domains. However, these systems hit critical reasoning, coordination, and computational scaling bottlenecks as the size and complexity of their tasks grow. These limitations hinder multi-agent systems from achieving high-throughput processing for highly parallelizable tasks, despite the availability of parallel computing and reasoning primitives in the underlying LLMs. We introduce the Agent-Parallel Workload Architecture (APWA), a distributed multi-agent system architecture designed for the efficient processing of heavily parallelizable agentic workloads. APWA facilitates parallel execution by decomposing workflows into non-interfering subproblems that can be processed using independent resources without cross-communication. It supports heterogeneous data and parallel processing patterns, and it accommodates tasks from a wide breadth of domains. In our evaluation, we demonstrate that APWA can dynamically decompose complex queries into parallelizable workflows and scales on larger tasks in settings where prior systems fail completely.