A Survey on Agent Workflow -- Status and Future (2508.01186v1)

Abstract: In the age of LLMs, autonomous agents have emerged as a powerful paradigm for achieving general intelligence. These agents dynamically leverage tools, memory, and reasoning capabilities to accomplish user-defined goals. As agent systems grow in complexity, agent workflows-structured orchestration frameworks-have become central to enabling scalable, controllable, and secure AI behaviors. This survey provides a comprehensive review of agent workflow systems, spanning academic frameworks and industrial implementations. We classify existing systems along two key dimensions: functional capabilities (e.g., planning, multi-agent collaboration, external API integration) and architectural features (e.g., agent roles, orchestration flows, specification languages). By comparing over 20 representative systems, we highlight common patterns, potential technical challenges, and emerging trends. We further address concerns related to workflow optimization strategies and security. Finally, we outline open problems such as standardization and multimodal integration, offering insights for future research at the intersection of agent design, workflow infrastructure, and safe automation.

• The paper presents a comprehensive survey comparing over 20 agent workflow systems, highlighting key functionalities and architectural differences.
• It evaluates functional capabilities such as planning, multi-agent collaboration, and API integration while analyzing deployment metrics across the systems.
• The study identifies optimization strategies and security challenges, advocating for standardization and modular architectures in future implementations.

A Survey on Agent Workflow: Status and Future

Introduction

In the landscape of AI-driven systems, autonomous agents stand out as a pivotal advancement toward achieving general intelligence. This paper explores the multifaceted domain of agent workflows, a critical aspect enabling scalable, integrated, and secure behaviors in complex AI systems. The survey comprehensively reviews academic and industrial frameworks, delineating the structural dimensions across which these workflows are organized and implemented. The discussion extends into functional capabilities and architectural features, juxtaposing over 20 systems for a robust comparative analysis. The paper identifies technical challenges, potential optimizations, and the imperative of security in agent workflows.

Functional Capabilities and Architectural Features

Agent workflows are dissected across two primary dimensions: functional capabilities and architectural features. Significant capabilities include planning, multi-agent collaboration, and external API integration. These are foundational to an agent's ability to perform user-defined tasks autonomously and securely. Architecturally, the workflows are characterized by their roles, orchestration flows, and specification languages, facilitating the seamless integration of LLMs, tool use, and interactive engagement with users and other agents.

The paper further elaborates on the need for a multi-layer architecture—a paradigm comprising UI/UX for intuitive interaction, workflow management for task execution, and agent collaboration for robust multi-agent ecosystems. Such frameworks underpin the efficiency and adaptability of agent workflows, enabling systems to tackle complex tasks in dynamic environments.

Comparative Analysis of Agent Workflow Systems

The survey provides an incisive comparative analysis of 24 agent workflow systems, evaluating them against metrics such as planning capabilities, tool utilization, memory use, and cross-platform deployment. Key insights emerge regarding support for self-reflection, custom tool integration, and open-source availability, revealing insights into the diversity and scope of these systems.

The architectural comparison highlights varied implementations of agent roles, flow structures (data vs. control), and deployment modalities. These findings underscore the heterogeneity in current practices and the lack of standardization across systems, a significant barrier to interoperability and scalability in agent workflows.

Optimization and Security

Optimization strategies in agent workflows are scarce but necessary for enhancing scheduling, resource allocation, and workflow execution. The paper discusses manual reconstruction, heuristic algorithms, Bayesian optimization, and novel generative optimizers as methods to achieve efficient workflow management.

Security in agent workflows is dissected into internal and external categories, covering tool interactions, MCP vulnerabilities, MAS collusion risks, and memory security threats. These insights are crucial for developing resilient systems in an era where the integration of AI with external environments is increasingly commonplace.

Applications and Future Directions

Agent workflows find applications across diverse sectors such as healthcare, urban planning, finance, education, and law. In each domain, workflows enable domain-specific tool use, collaboration, and real-time decision-making, facilitating intelligent, task-driven applications that augment human capabilities.

The future direction for agent workflows must address the pressing need for standardization and modular architectures. The survey stresses the importance of crafting unified frameworks, adaptive tool usage, and cross-platform interoperability to support advanced, context-aware, multi-modal workflows.

Conclusion

The survey systematically reviews the burgeoning field of agent workflows, providing a detailed comparative analysis of their functionalities and architectures. Standardization, modularity, and orchestration emerge as key imperatives, with the potential to transform scattered practices into cohesive systems for intelligent applications. This paper aims to ignite further innovation and convergence in agent workflows, ultimately laying the groundwork for robust, general AI systems.