multi-agent orchestration

Existing multi-agent collaborative systems are hindered by static workflows, sequential scheduling, and heterogeneous interfaces, leading to high complexity and poor scalability. This work proposes Agent-as-Tool, a unified paradigm that abstracts both agents and tools into a standardized, learnable action space, and introduces ParaManager—a lightweight coordinator enabling state-aware parallel subtask decomposition, delegation, and asynchronous execution. By unifying communication protocols and incorporating explicit state feedback, the framework facilitates efficient multi-agent collaboration. A two-stage training strategy—combining supervised fine-tuning with a recovery mechanism and reinforcement learning—optimizes task success rate, protocol compliance, response diversity, and reasoning efficiency. Experiments demonstrate that ParaManager achieves strong performance across multiple benchmarks and exhibits robust generalization to unseen agent pools.

This work addresses the challenges of low coordination efficiency, lack of auditability, and insufficient policy compliance in multi-agent systems for complex tasks by proposing a unified orchestration framework that integrates planning, policy execution, state management, and observability mechanisms. The framework introduces two novel protocols—the Model Context Protocol and Agent2Agent Protocol—to enable standardized, extensible communication among agents and between agents and external tools. By supporting policy governance and end-to-end traceability, the proposed architecture provides a practical, transparent, and accountable blueprint for deploying large-scale multi-agent systems within enterprise AI ecosystems.

This work addresses the current lack of open-source infrastructure capable of efficiently training and evaluating large-scale agents on complex tasks such as software engineering and computer operation. To this end, we propose a three-service decoupled architecture tailored for agent-environment interaction workloads, which separates the system into three independent services—model, agent, and environment—enabling fine-grained task scheduling, dynamic resource allocation, and unified interface communication. This design allows each component to scale independently and configure resources flexibly, significantly improving training efficiency and resource utilization. Experimental results demonstrate that the system can stably support tens of thousands of concurrent agent tasks, thereby filling a critical gap in infrastructure for large-scale agent training.

This work addresses the high latency in multi-agent systems caused by multi-step reasoning and redundant agent invocations during parallel execution, which often fails to meet real-time requirements. To this end, the authors propose LAMaS, a novel framework that introduces explicit latency supervision signals into multi-agent orchestration for the first time. LAMaS employs a learning-driven controller to construct an execution topology graph and leverages critical path analysis to optimize parallel scheduling. This approach departs from conventional paradigms centered on task performance or cost, instead prioritizing latency reduction along the critical path. Experimental results demonstrate that LAMaS reduces critical path length by 38%–46% compared to state-of-the-art methods across multiple benchmarks, while maintaining or even improving task performance.

Existing approaches to automatic multi-agent system design suffer from complex orchestration, limited global reasoning capabilities, and ill-defined boundaries of their advantages. This work proposes MAS-Orchestra, a novel framework that formulates multi-agent orchestration as a function-call-based global reinforcement learning problem, enabling end-to-end generation of complete systems in a single pass. To systematically evaluate task characteristics, the authors introduce MASBENCH, a benchmark assessing performance across five dimensions: Depth, Horizon, Breadth, Parallelism, and Robustness. Experimental results demonstrate consistent performance gains on tasks such as mathematical reasoning and multi-hop question answering. Furthermore, the study reveals that the benefits of multi-agent collaboration are not universal but critically depend on task structure, verification mechanisms, and individual agent capabilities.

This work addresses the challenges faced by large language model (LLM) agents operating over flat tool registries—namely, combinatorial explosion in decision space, context saturation, and degraded routing accuracy. To overcome these limitations, the authors propose a skill-tree-based hierarchical architecture that separates routing logic at internal nodes from execution at leaf nodes. Inspired by pushdown automata, the framework incorporates a LIFO stack-frame memory model and a lazy capability discovery mechanism, enabling isolated execution paths and scalable context management. The approach supports manifest-driven single-step execution loops and formal state modeling, significantly improving routing accuracy while reducing memory footprint and prompt costs under conditions of tool proliferation, multi-step workflows, and prompt exposure. This design meets enterprise-grade requirements for isolation and scalability.

This study addresses the challenge of ensuring traceability, controllability, and correctness of large language model–driven agents within business processes while preserving their autonomy. To this end, the work proposes the first multidimensional attribute classification framework specifically designed for agent orchestration, integrating principles from business process management to strike a balance between agent autonomy and system robustness. Complementing the framework, the authors introduce qualitative decision-making guidelines and quantitative evaluation metrics. The efficacy of the proposed approach is empirically validated through multi-agent experiments in a predictive light-sensing scenario, demonstrating its capacity to support both theoretical inquiry and practical deployment of orchestrated intelligent agents in real-world applications.

This study addresses the challenge of automating workflows in complex industries—such as logistics, healthcare, and construction—where processes are fragmented across heterogeneous tools and involve multi-party collaboration. The work proposes orchestration as a core abstraction to enable effective automation by dynamically coordinating multi-step tasks, enforcing domain-specific constraints, managing human approvals, and integrating legacy systems. It introduces the novel concept of “orchestration bottlenecks” and develops a theoretical framework that unifies multi-agent systems, workflow modeling, constraint reasoning, and human–AI collaboration, while exposing critical gaps in current multi-agent approaches at the orchestration level. Based on distinct sources of operational friction across domains, the paper advocates for targeted architectural safeguards—such as constraint enforcement or explainability—and phased implementation strategies to provide actionable pathways for automation in complex operational environments.

Current distributed agent systems lack a unified, implementation-agnostic runtime architecture, making it difficult to effectively govern intent, permissions, uncertainty, behavioral coordination, and traceability. This work proposes an Agent Operating System (AOS), which introduces the first dual-plane reference architecture for distributed agent systems: a control and governance plane responsible for policy enforcement, auditing, and human oversight, and a runtime and coordination plane handling workflow execution, model routing, memory coordination, and scheduling. By standardizing interfaces that decouple governance from runtime responsibilities, AOS enables flexible composition of heterogeneous components and formalizes core concepts, interface objects, and deployment configurations. This architectural foundation supports the development of trustworthy, auditable, scalable, and interoperable agent systems, while also outlining key research challenges in the field.

Existing evaluation methods struggle to disentangle the quality of task orchestration in multi-agent systems from confounding factors such as agent capabilities and environmental noise, while real-world execution incurs prohibitive costs. To address this, this work proposes OrchBench—a deterministic simulation-based benchmarking platform that models task dependencies via directed acyclic graphs and enables isolated, efficient assessment of orchestration plans. OrchBench achieves the first interpretable evaluation of orchestration quality with dramatically reduced overhead: requiring only 1.3% of the tokens and 10.3% of the time compared to real execution, while maintaining high fidelity (Pearson r = 0.816). Furthermore, it reveals that information retention rate is more critical to performance than simply increasing the number of agents.