@inproceedings{yue-etal-2026-static,
title = "From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for {LLM} Agents",
author = "Yue, Ling and
Bhandari, Kushal Raj and
Ko, Ching-Yun and
Patel, Dhaval C and
Lin, Shuxin and
Zhou, Nianjun and
Gao, Jianxi and
Chen, Pin-Yu and
Pan, Shaowu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1393/",
pages = "29630--29658",
ISBN = "979-8-89176-462-0",
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 classify the literature according to when workflow structure is determined. 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 categorize existing work along three dimensions: when structure is determined, which aspect of the workflow is optimized, and which evaluation signals govern optimization (e.g., task metrics, verifier signals, human preferences, or trace-derived feedback). We also distinguish reusable workflow templates, run-specific realized graphs, and execution traces, separating design-time choices from runtime behavior. Finally, we outline a structure-aware evaluation perspective that complements downstream task metrics with graph-level properties, execution cost, robustness, and structural variation across inputs. This work aims to establish a principled vocabulary, a unified framework for situating new methods, a systematic comparison of existing work, and a minimum reporting protocol for LLM agent workflow optimization."
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<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 classify the literature according to when workflow structure is determined. 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 categorize existing work along three dimensions: when structure is determined, which aspect of the workflow is optimized, and which evaluation signals govern optimization (e.g., task metrics, verifier signals, human preferences, or trace-derived feedback). We also distinguish reusable workflow templates, run-specific realized graphs, and execution traces, separating design-time choices from runtime behavior. Finally, we outline a structure-aware evaluation perspective that complements downstream task metrics with graph-level properties, execution cost, robustness, and structural variation across inputs. This work aims to establish a principled vocabulary, a unified framework for situating new methods, a systematic comparison of existing work, and a minimum reporting protocol for LLM agent workflow optimization.</abstract>
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%0 Conference Proceedings
%T From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents
%A Yue, Ling
%A Bhandari, Kushal Raj
%A Ko, Ching-Yun
%A Patel, Dhaval C.
%A Lin, Shuxin
%A Zhou, Nianjun
%A Gao, Jianxi
%A Chen, Pin-Yu
%A Pan, Shaowu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F yue-etal-2026-static
%X 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 classify the literature according to when workflow structure is determined. 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 categorize existing work along three dimensions: when structure is determined, which aspect of the workflow is optimized, and which evaluation signals govern optimization (e.g., task metrics, verifier signals, human preferences, or trace-derived feedback). We also distinguish reusable workflow templates, run-specific realized graphs, and execution traces, separating design-time choices from runtime behavior. Finally, we outline a structure-aware evaluation perspective that complements downstream task metrics with graph-level properties, execution cost, robustness, and structural variation across inputs. This work aims to establish a principled vocabulary, a unified framework for situating new methods, a systematic comparison of existing work, and a minimum reporting protocol for LLM agent workflow optimization.
%U https://aclanthology.org/2026.findings-emnlp.1393/
%P 29630-29658
Markdown (Informal)
[From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents](https://aclanthology.org/2026.findings-emnlp.1393/) (Yue et al., Findings 2026)
ACL
- Ling Yue, Kushal Raj Bhandari, Ching-Yun Ko, Dhaval C Patel, Shuxin Lin, Nianjun Zhou, Jianxi Gao, Pin-Yu Chen, and Shaowu Pan. 2026. From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 29630–29658, Budapest, Hungary. Association for Computational Linguistics.