HAWK: A Hierarchical Workflow Framework for Multi-Agent Collaboration

Abstract: Contemporary multi-agent systems encounter persistent challenges in cross-platform interoperability, dynamic task scheduling, and efficient resource sharing. Agents with heterogeneous implementations often lack standardized interfaces; collaboration frameworks remain brittle and hard to extend; scheduling policies are static; and inter-agent state synchronization is insufficient. We propose Hierarchical Agent Workflow (HAWK), a modular framework comprising five layers-User, Workflow, Operator, Agent, and Resource-and supported by sixteen standardized interfaces. HAWK delivers an end-to-end pipeline covering task parsing, workflow orchestration, intelligent scheduling, resource invocation, and data synchronization. At its core lies an adaptive scheduling and optimization module in the Workflow Layer, which harnesses real-time feedback and dynamic strategy adjustment to maximize utilization. The Resource Layer provides a unified abstraction over heterogeneous data sources, large models, physical devices, and third-party services&tools, simplifying cross-domain information retrieval. We demonstrate HAWK's scalability and effectiveness via CreAgentive, a multi-agent novel-generation prototype, which achieves marked gains in throughput, lowers invocation complexity, and improves system controllability. We also show how hybrid deployments of LLMs integrate seamlessly within HAWK, highlighting its flexibility. Finally, we outline future research avenues-hallucination mitigation, real-time performance tuning, and enhanced cross-domain adaptability-and survey prospective applications in healthcare, government, finance, and education.

• The paper introduces the HAWK framework, a novel hierarchical approach that integrates layered agent workflows with 16 standardized interfaces for seamless collaboration.
• The methodology emphasizes dynamic task scheduling and efficient resource sharing via a five-layered architecture applied in the CreAgentive prototype.
• Performance evaluations indicate improved throughput, reduced complexity, and scalable integration across diverse multi-agent system applications.

HAWK: A Hierarchical Workflow Framework for Multi-Agent Collaboration

The paper introduces the Hierarchical Agent Workflow (HAWK) framework aimed at addressing key challenges within multi-agent systems (MAS), such as cross-platform interoperability, dynamic task scheduling, and efficient resource sharing. It is structured into five functional layers—User, Workflow, Operator, Agent, and Resource—and is distinguished by sixteen standardized interfaces to ensure modularity and flexibility.

Framework Overview

The HAWK framework encompasses all critical aspects of agent-based systems, from client-side workflow specification and submission through to task execution and resource provisioning. The multi-layer approach enhances scalability and allows for seamless integration with external systems, including hybrid deployments of LLMs.

Figure 1: Framework of Hierarchical Agent WorKflow (HAWK)

Layers

1. User Layer: Provides task submission interfaces and translates inputs for subsequent processing. It allows flexibility in user interface customization, thus promoting reuse across different scientific domains.
2. Workflow Layer: Responsible for the orchestration and optimization of workflows based on parsed tasks received from the User Layer, emphasizing planning, execution, monitoring, and optimization.
3. Operator Layer: Focuses on dynamic task scheduling and execution using six core modules: Environment, Memory, Task Management, Task Optimizer, Reasoning, and Security. These elements support efficient and secure agent operations.
4. Agent Layer: Manages agent tasks related to Specification, Publication, Registration, and Discovery, enabling flexible deployment across diverse environments while maintaining overall scheduling strategy cohesion.
5. Resource Layer: Supplies the Operator and Agent Layers with heterogeneous resources like data, models, physical devices, and third-party services, facilitating standardized resource invocation and cross-domain information retrieval.

Interfaces

HAWK defines 16 interfaces that standardize communication protocols within the framework, promoting interoperability and facilitating end-to-end coordination from user requests to agent collaboration. These interfaces ensure adaptability and extensibility across various application scenarios, such as resource access and task scheduling.

Implementation: CreAgentive

To validate the HAWK framework, the authors implemented CreAgentive, a multi-agent prototype for novel generation. It demonstrates HAWK's capabilities in orchestrating agents to produce structured narratives and showcases its flexibility and adaptability.

Figure 2: The Workflow of the CreAgentive

CreAgentive Workflow

CreAgentive iteratively generates narrative content by coordinating agents through goal-driven stages, ensuring coherent story development. It employs long-term and short-term goals to guide agents in constructing narratives chapter by chapter.

Key Components

• Environment Agent: Manages the evolving state of the story, enabling versioned context updates that maintain narrative traceability.
• Decision Agent: Utilizes a dual-system cognitive framework to evaluate candidate storylines and select optimum trajectories.
• Writer Agent: Transforms selected narrative plans into coherent chapters using LLMs.
• Ending Determination Agent: Checks narrative progress against predefined endings, signaling workflow termination upon completion.

Performance Evaluation

The paper reports robust results in throughput improvement, invocation complexity reduction, and enhanced system controllability. It suggests future enhancements, like integration of retrieval-augmented generation (RAG) mechanisms and multi-modal collaboration protocols for effective cross-domain application.

Conclusion

HAWK offers a comprehensive and modular framework for MAS, addressing critical challenges inherent to agent coordination and resource orchestration. Its layered architecture supports seamless integration and evolution, empowering agents with adaptive scheduling and standardized resource access.

HAWK is poised for deployment across various domains, such as healthcare, government services, and finance, advancing the integration of intelligent AI entities into cross-sector applications. Considerable potential remains for further research in hallucination mitigation and real-time performance tuning to strengthen its applicability within complex environments.

In summary, the HAWK framework represents a foundational architecture capable of driving intelligent and synergistic multi-agent systems toward greater collaboration across diverse technological domains.