[2603.22386] From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents

Computer Science > Artificial Intelligence arXiv:2603.22386 (cs) [Submitted on 23 Mar 2026] Title:From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents Authors:Ling Yue, Kushal Raj Bhandari, Ching-Yun Ko, Dhaval Patel, Shuxin Lin, Nianjun Zhou, Jianxi Gao, Pin-Yu Chen, Shaowu Pan View a PDF of the paper titled From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents, by Ling Yue and 8 other authors View PDF HTML (experimental) Abstract:Large language model (LLM)-based systems are becoming increasingly popular for solving tasks by constructing executable workflows that interleave LLM calls, information retrieval, tool use, code execution, memory updates, and verification. This survey reviews recent methods for designing and optimizing such workflows, which we treat as agentic computation graphs (ACGs). We organize the literature based on when workflow structure is determined, where structure refers to which components or agents are present, how they depend on each other, and how information flows between them. This lens distinguishes static methods, which fix a reusable workflow scaffold before deployment, from dynamic methods, which select, generate, or revise the workflow for a particular run before or during execution. We further organize prior work along three dimensions: when structure is determined, what part of the workflow is optimized, and which evaluation signals guide optimizatio...