HAWK: A Hierarchical Workflow Framework for Multi-Agent Collaboration (2507.04067v1)

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 HAWK, a modular five-layer workflow framework that overcomes limitations in multi-agent systems through dynamic scheduling and resource abstraction.
• The framework employs sixteen standardized interfaces and adaptive optimization to ensure scalable and interoperable agent collaboration.
• Empirical evaluation via the CreAgentive system validates HAWK’s robust performance, achieving over 92% module reliability and improved narrative generation.

HAWK: A Hierarchical Workflow Framework for Multi-Agent Collaboration

The HAWK framework introduces a modular, hierarchical architecture designed to overcome persistent shortcomings in contemporary multi-agent systems, such as cross-platform interoperability, dynamic scheduling, resource abstraction, and state synchronization. This essay summarizes the core innovations, architectural design, implementation patterns, empirical findings, and implications of HAWK from a research perspective.

Architectural Overview and Design Rationales

HAWK is characterized by a five-layer architecture—User, Workflow, Operator, Agent, and Resource—supported by sixteen standardized interfaces that provide end-to-end modularity and extensibility. Each layer addresses specific responsibilities, intentionally decoupled to enable independent evolution and compositional flexibility:

• User Layer: Handles interface translation and parsing of user requests, abstracting away from backend complexity. This enables the integration of various frontends or APIs while preserving uniform downstream semantics.
• Workflow Layer: Manages workflow orchestration, execution monitoring, global optimization, and high-level planning. Adaptive scheduling takes real-time feedback into account, supporting dynamic adjustment of task decomposition and execution.
• Operator Layer: Contains runtime mechanisms and governs task execution, including context management, persistent memory, policy-driven optimization, security, and fault management. Its modularization facilitates scalable fine-grained control.
• Agent Layer: Implements agent lifecycles (Specification, Publication, Registration, Discovery) while enabling discovery and collaboration in heterogeneous environments. This allows agents to operate autonomously on diverse platforms without sacrificing governance.
• Resource Layer: Abstracts heterogeneous resources—structured/unstructured data, LLMs, hardware devices, and external toolchains—through unified interfaces. This enables seamless integration of new data sources, models, or devices without rearchitecting higher layers.

The specification of sixteen protocolized interfaces ensures that modules and layers can interact predictably and are swappable or extensible. This protocolization lowers barriers to cross-vendor interoperability, a recurrent limitation of competing frameworks.

Comparative Analysis with Prior Art

The paper situates HAWK within a detailed comparison of leading agent workflow platforms, assessed through a "capability heatmap" covering planning, tool use, multi-agent collaboration, memory, interfaces, reflection, extensibility, cross-platform deployment, and distributed execution. The review highlights that most existing solutions, such as AutoGen (Wu et al., 2023), LangGraph (Duan et al., 27 Nov 2024), and MetaGPT (Hong et al., 2023), offer only partial support for the spectrum of features needed in practical deployments. HAWK’s primary differentiators are its:

• Protocol-agnosticism: Integration atop MCP, ANP, and A2A allows abstraction across contemporary messaging and agent-network conventions.
• Unified resource layer: Enables plug-and-play adaptation to both cloud-native and edge/embedded hardware environments, promoting cross-domain applicability.
• Workflow and scheduling intelligence: Adaptive task allocation and optimization mechanisms leverage real-time system feedback, overcoming the static nature of prior approaches.

Implementation: CreAgentive System

To empirically validate HAWK, the authors implemented CreAgentive, a multi-agent, LLM-driven novel composition system. This system operationalizes HAWK’s architecture in a demonstration that foregrounds collaborative, iterative text generation. The workflow comprises the following stages:

1. Initialization: Resource, environment, character, and outline loading; memory versioning.
2. Hierarchical Planning: Long-term goals (from the story outline) and agent-driven short-term goals (generated via chain-of-thought mechanisms).
3. Parallel Agency: Agents execute individual plans, producing diverse candidate trajectories for each chapter, utilizing persistent memory and environment simulation.
4. Decision Making: A Decision Agent, inspired by the TELLER architecture (incorporating a differentiable DNF logic layer), selects the optimal narrative trajectory from candidates.
5. Language Generation: The Writer Agent synthesizes the selected trajectory into text via targeted LLM prompts.
6. Audit and Update: Environment and character states are versioned and archived, ensuring full traceability and facilitating rollback or review.
7. Termination Evaluation: An Ending Determination Agent, using both symbolic and LLM assessments, checks for convergence on the story’s planned resolution.

Empirical Evaluation:

• LLM Performance: Deepseek-V3 maintained the strongest chapter continuity and lowest hallucination rate, while Qwen-32B and GLM-4-9B excelled in plan reasoning but underperformed in longer narrative tasks.
• System Robustness: Modules executed with >92% and supported up to 5 concurrent story generations without quality loss, indicating strong scalability and resilience to isolated failures.
• Throughput: 10-chapter stories averaged 80 minutes to generate, with model selection significantly influencing narrative fluency and logical consistency.

Numerical Results:

Model	Avg. Chapters	Continuity	Weaknesses
Deepseek-V3	20	Strong	Over-imaginative
Qwen-32B	10	Moderate	Inconsistent formatting
GLM-4-9B	6	Weak	Less fluent

Implications and Future Directions

HAWK’s design addresses pressing challenges in agent-based workflow systems from both a theoretical and practical perspective:

• Standardization and Extensibility: The explicit protocolization of all communication and control pathways enhances composability, facilitating extension to new domains, platforms, and agents, and lowering vendor lock-in.
• Adoption Scenarios: The authors outline deployment paths in healthcare (medical IoT, clinical workflow), government (cross-agency orchestration), finance (high-throughput analysis), and education (adaptive platforms). The abstracted resource model enables integration of domain-specific data, devices, and models without upstream redesign.
• Model Selection and Hybridization: Empirical results support using a hybrid LLM approach—different models for planning, narrative generation, and decision-making—optimizing both fluency and logical coherence depending on module-specific demands.
• Scalability: HAWK’s layered abstraction and protocolized interfaces support distribution at scale, facilitating millions of agents across hybrid cloud and edge networks without bottlenecks in orchestration or resource contention.

Limitations and Prospects:

• Occasional LLM hallucination and rule-violation in content generation remain unsolved, to be addressed by further workflow monitoring and constraint mechanisms.
• Bottlenecks at high concurrency may require further optimization in the Operator and Resource layers.
• Extension to complex, regulated domains demands additional robustness and verification processes, as recognized in the authors' roadmap.

Theoretical and Practical Impact

The HAWK framework represents a methodologically rigorous, implementation-ready blueprint for multi-agent collaboration. The separation of concerns, adaptive optimization, and resource abstraction are likely to inform both best practices and future industry or regulatory standards in agent orchestration. The approach enables new forms of large-scale, cross-domain AI integration—e.g., enabling medical agents to influence policy or educational AIs to interact with labor market analytics.

Further, HAWK’s architectural compatibility with retrieval-augmented generation (RAG) patterns lays the groundwork for real-time, heterogeneous knowledge fusion across industry, science, and government applications. This points to a future where collaborative agentic systems, mediated by frameworks like HAWK, become foundational to AI infrastructure—enabling adaptive, secure, and explainable automation at societal scale.