# Flow: Modularized Agentic Workflow Automation

arXiv: https://arxiv.org/abs/2501.07834 · HTML: https://mlsanity.com/paper/2501.07834/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 19 of its 36 references that are in the map.
- First posted on arXiv 14 Jan 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 19 of 19)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://mlsanity.com/paper/2308.10848/) (2023)
- [Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models](https://mlsanity.com/paper/2310.04406/) (2023)
- [LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models](https://mlsanity.com/paper/2212.04088/) (2022)
- [AFlow: Automating Agentic Workflow Generation](https://mlsanity.com/paper/2410.10762/) (2024)
- [A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration](https://mlsanity.com/paper/2310.02170/) (2023)
- [Scaling Large Language Model-based Multi-Agent Collaboration](https://mlsanity.com/paper/2406.07155/) (2024)
- [ADaPT: As-Needed Decomposition and Planning with Language Models](https://mlsanity.com/paper/2311.05772/) (2023)
- [Agents: An Open-source Framework for Autonomous Language Agents](https://mlsanity.com/paper/2309.07870/) (2023)
- [Language Agents as Optimizable Graphs](https://mlsanity.com/paper/2402.16823/) (2024)
- [TDAG: A Multi-Agent Framework based on Dynamic Task Decomposition and Agent Generation](https://mlsanity.com/paper/2402.10178/) (2024)
- [DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy](https://mlsanity.com/paper/2310.18659/) (2023)
- [Rational Decision-Making Agent with Internalized Utility Judgment](https://mlsanity.com/paper/2308.12519/) (2023)

## Built on by (most cited first, 30 of 34)

- [From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents](https://mlsanity.com/paper/2603.22386/) (2026)
- [Heterogeneous Swarms: Jointly Optimizing Model Roles and Weights for Multi-LLM Systems](https://mlsanity.com/paper/2502.04510/) (2025)
- [RobustFlow: Towards Robust Agentic Workflow Generation](https://mlsanity.com/paper/2509.21834/) (2025)
- [Murakkab: Resource-Efficient Agentic Workflow Orchestration in Cloud Platforms](https://mlsanity.com/paper/2508.18298/) (2025)
- [Position: The Real Barrier to LLM Agent Usability is Agentic ROI](https://mlsanity.com/paper/2505.17767/) (2025)
- [Sherlock: Reliable and Efficient Agentic Workflow Execution](https://mlsanity.com/paper/2511.00330/) (2025)
- [Aime: Towards Fully-Autonomous Multi-Agent Framework](https://mlsanity.com/paper/2507.11988/) (2025)
- [Lifting Traces to Logic: Programmatic Skill Induction with Neuro-Symbolic Learning for Long-Horizon Agentic Tasks](https://mlsanity.com/paper/2605.01293/) (2026)
- [Tiered Agentic Oversight: A Hierarchical Multi-Agent System for Healthcare Safety](https://mlsanity.com/paper/2506.12482/) (2025)
- [PowerChain: A Verifiable Agentic AI System for Automating Distribution Grid Analyses](https://mlsanity.com/paper/2508.17094/) (2025)
- [On Training Large Language Models for Long-Horizon Tasks: An Empirical Study of Horizon Length](https://mlsanity.com/paper/2605.02572/) (2026)
- [InfoSeeker: A Scalable Hierarchical Parallel Agent Framework for Web Information Seeking](https://mlsanity.com/paper/2604.02971/) (2026)
- [Multi-View Encoders for Performance Prediction in LLM-Based Agentic Workflows](https://mlsanity.com/paper/2505.19764/) (2025)
- [ALLOY: Generating Reusable Agent Workflows from User Demonstration](https://mlsanity.com/paper/2510.10049/) (2025)
- [Can Agents Secure Hardware? Evaluating Agentic LLM-Driven Obfuscation for IP Protection](https://mlsanity.com/paper/2604.13298/) (2026)
- [SEVADE: Self-Evolving Multi-Agent Analysis with Decoupled Evaluation for Hallucination-Resistant Irony Detection](https://mlsanity.com/paper/2508.06803/) (2025)
- [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025)
- [SymbOmni: Evolving Agentic Omni Models via Symbolic Concept Learning](https://mlsanity.com/paper/2607.12042/) (2026)
- [Risk-Sensitive Agent Compositions](https://mlsanity.com/paper/2506.04632/) (2025)
- [Convergence dynamics of Agent-to-Agent Interactions with Misaligned objectives](https://mlsanity.com/paper/2511.08710/) (2025)
- [FutureWeaver: Planning Test-Time Compute for Multi-Agent Systems with Modularized Collaboration](https://mlsanity.com/paper/2512.11213/) (2025)
- [GLOW: Graph-Language Co-Encoding for Agentic Workflow Performance Prediction](https://mlsanity.com/paper/2512.15751/) (2025)
- [APWA: A Distributed Architecture for Parallelizable Agentic Workflows](https://mlsanity.com/paper/2605.15132/) (2026)
- [How Task Structure Limits Multi-Agent Success: An Information-Theoretic Analysis](https://mlsanity.com/paper/2606.13733/) (2026)
- [Coupled Hierarchical Search over Topology and Execution for Agentic Workflow Synthesis](https://mlsanity.com/paper/2607.21609/) (2026)
- [MEGA: Self-Evolving Agent Optimization Infrastructure via Wisdom Graph](https://mlsanity.com/paper/2608.10504/) (2026)
- [Componentization: Decomposing Monolithic LLM Responses into Manipulable Semantic Units](https://mlsanity.com/paper/2509.08203/) (2025)
- [Privacy-Preserving EHR Data Transformation via Geometric Operators: A Human-AI Co-Design Technical Report](https://mlsanity.com/paper/2603.22954/) (2026)
- [AgentCo-op: Retrieval-Based Synthesis of Interoperable Multi-Agent Workflows](https://mlsanity.com/paper/2605.20425/) (2026)
- [Beyond Generalist LLMs: Specialist Agentic Systems for Structured Code Workflow Execution](https://mlsanity.com/paper/2607.14456/) (2026)

## Abstract (arXiv)

Multi-agent frameworks powered by large language models (LLMs) have demonstrated great success in automated planning and task execution. However, the effective adjustment of agentic workflows during execution has not been well studied. An effective workflow adjustment is crucial in real-world scenarios, as the initial plan must adjust to unforeseen challenges and changing conditions in real time to ensure the efficient execution of complex tasks. In this paper, we define workflows as an activity-on-vertex (AOV) graph, which allows continuous workflow refinement by LLM agents through dynamic subtask allocation adjustment based on historical performance and previous AOVs. To further enhance framework performance, we emphasize modularity in workflow design based on evaluating parallelism and dependency complexity. With this design, our proposed multi-agent framework achieves efficient concurrent execution of subtasks, effective goal achievement, and enhanced error tolerance. Empirical results across various practical tasks demonstrate significant improvements in the efficiency of multi-agent frameworks through dynamic workflow refinement and modularization. The code is available at: https://github.com/tmllab/2025_ICLR_FLOW.