A Survey on Agent Workflow -- Status and Future

2025-08-02

AI Summary

In the era of large language models, agent workflows face critical challenges in scalability, controllability, and security. To address these, this paper presents a systematic literature review and proposes, for the first time, a dual-dimensional taxonomy—spanning functional capabilities (task planning, multi-agent collaboration, tool integration) and architectural characteristics (role definition, orchestration process, specification languages). Through comparative analysis of over twenty representative academic and industrial systems, we identify recurring design patterns and persistent technical bottlenecks. We further introduce security-enhanced orchestration optimization strategies and pinpoint core gaps, including the lack of standardization and insufficient multimodal integration. This work establishes a foundational theoretical framework and practical guidelines for the design, evaluation, and evolution of agent workflows, advancing the field toward structured, trustworthy, and multimodal-cooperative paradigms.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent/AI Theories and Architectures
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

In the age of large language models (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.

Problem

• Surveying agent workflow systems for scalable AI behaviors
• Classifying systems by functional and architectural features
• Addressing workflow optimization, security, and future challenges

Innovation

• Dynamic tool and memory utilization by agents
• Structured orchestration for scalable AI behaviors
• Comprehensive classification of workflow systems