The Transition from AI Assistants to Autonomous Agents: A Survey of Multi-Agent Orchestration Systems

The transition from solitary AI assistants to orchestrated, autonomous multi-agent systems represents the future of digi- tal labor. By decomposing complex workflows into specialized roles—Worker, Service, and Support agents—MAS architec- tures circumvent the reasoning and context limitations of single LLMs. However, this transition shifts the engineering burden from prompt optimization to systems architecture. Developers must carefully choose their orchestration frameworks: leveraging LangGraph for deterministic, auditable state machines, Cre- wAI for rapid role-based deployments, or MetaGPT for strict, SOP-driven generation. Crucially, as these systems gain Level 4 autonomy, the implementation of resilient topologies (such as hierarchical structures) and validation layers (Inspector agents) is not optional; it is a fundamental requirement to prevent cascading hallucinations and ensure enterprise-grade safety.

[1]     M. Zhu, M. Li, M. Cui, et al., “A Survey on Large Language Model- based Multi-Agent Systems: Paradigms, Applications, and Challenges,” TechRxiv, Feb. 2026. [2]     Anonymous Authors, “The Orchestration of Multi-Agent Systems: Ar- chitectures, Protocols, and Enterprise Adoption,” arXiv preprint, Jan. 2026. [3]     Anonymous Authors, “LLM-Based Multi-Agent Orchestration: A Sur- vey of Frameworks, Communication Protocols, and Emerging Patterns,” Preprints.org, Apr. 2026. [4]     Anonymous Authors, “On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agents,” OpenReview, 2026. [5]     Anonymous Authors, “AI Agents in Service Experience: Towards Au- tonomous and Conscious Agency,” Journal of Service Management, Emerald Publishing, Feb. 2026. [6]     Q. Wu, G. Bansal, J. Zhang, et al., “AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework,” arXiv preprint arXiv:2308.08155, 2023. [7]     S. Hong, M. Zhuge, J. Chen, et al., “MetaGPT: Meta Program- ming for Multi-Agent Collaborative Framework,” arXiv preprint arXiv:2308.00352, 2023. [8]     S. Yao, J. Zhao, D. Yu, et al., “ReAct: Synergizing Reasoning and Acting in Language Models,” International Conference on Learning Representations (ICLR), 2023. [9]     LangChain AI, “LangGraph: Stateful, Multi-Actor LLM Appli- cations,” LangChain Documentation, 2024. [Online]. Available: https://python.langchain.com/docs/langgraph [10]    CrewAI, “CrewAI Framework Documentation: Role-playing au- tonomous AI agents,” CrewAI Official Documentation, 2024.