# RobustFlow: Towards Robust Agentic Workflow Generation

arXiv: https://arxiv.org/abs/2509.21834 · HTML: https://mlsanity.com/paper/2509.21834/

## Key facts

- Cited by 20 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 44 of its 69 references that are in the map.
- First posted on arXiv 26 Sep 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, 30 of 44)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023)
- [DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs](https://mlsanity.com/paper/1903.00161/) (2019)
- [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://mlsanity.com/paper/2308.10848/) (2023)
- [JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models](https://mlsanity.com/paper/2404.01318/) (2024)
- [AFlow: Automating Agentic Workflow Generation](https://mlsanity.com/paper/2410.10762/) (2024)
- [Automated Design of Agentic Systems](https://mlsanity.com/paper/2408.08435/) (2024)
- [SWIFT:A Scalable lightWeight Infrastructure for Fine-Tuning](https://mlsanity.com/paper/2408.05517/) (2024)
- [TextGrad: Automatic "Differentiation" via Text](https://mlsanity.com/paper/2406.07496/) (2024)
- [PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts](https://mlsanity.com/paper/2306.04528/) (2023)
- [Data Interpreter: An LLM Agent For Data Science](https://mlsanity.com/paper/2402.18679/) (2024)
- [AutoAgents: A Framework for Automatic Agent Generation](https://mlsanity.com/paper/2309.17288/) (2023)
- [Multi-agent Architecture Search via Agentic Supernet](https://mlsanity.com/paper/2502.04180/) (2025)
- [AgentSquare: Automatic LLM Agent Search in Modular Design Space](https://mlsanity.com/paper/2410.06153/) (2024)
- [The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore Non-Determinism](https://mlsanity.com/paper/2407.10457/) (2024)
- [T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step](https://mlsanity.com/paper/2312.14033/) (2023)
- [InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective](https://mlsanity.com/paper/2010.02329/) (2020)
- [Non-Determinism of "Deterministic" LLM Settings](https://mlsanity.com/paper/2408.04667/) (2024)
- [Evaluating the Robustness of Neural Language Models to Input Perturbations](https://mlsanity.com/paper/2108.12237/) (2021)

## Built on by (most cited first, 20 of 20)

- [ReCreate: Reasoning and Creating Domain Agents Driven by Experience](https://mlsanity.com/paper/2601.11100/) (2026)
- [From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents](https://mlsanity.com/paper/2603.22386/) (2026)
- [Harnessing Agentic Evolution](https://mlsanity.com/paper/2605.13821/) (2026)
- [FlowSteer: Towards Agents Designing Agentic Workflows via Reinforced Progressive Canvas Editing](https://mlsanity.com/paper/2602.01664/) (2026)
- [RemoteAgent: Bridging Vague Human Intents and Earth Observation with RL-based Agentic MLLMs](https://mlsanity.com/paper/2604.07765/) (2026)
- [Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application](https://mlsanity.com/paper/2606.12191/) (2026)
- [CoS: Towards Optimal Event Scheduling via Chain-of-Scheduling](https://mlsanity.com/paper/2511.12913/) (2025)
- [Learning to Compose for Cross-domain Agentic Workflow Generation](https://mlsanity.com/paper/2602.11114/) (2026)
- [Do We Always Need Query-Level Workflows? Rethinking Agentic Workflow Generation for Multi-Agent Systems](https://mlsanity.com/paper/2601.11147/) (2026)
- [PeroMAS: A Multi-agent System of Perovskite Material Discovery](https://mlsanity.com/paper/2602.13312/) (2026)
- [RemoteShield: Enable Robust Multimodal Large Language Models for Earth Observation](https://mlsanity.com/paper/2604.17243/) (2026)
- [Towards Direct Evaluation of Harness Optimizers via Priority Ranking](https://mlsanity.com/paper/2605.22505/) (2026)
- [GLOW: Graph-Language Co-Encoding for Agentic Workflow Performance Prediction](https://mlsanity.com/paper/2512.15751/) (2025)
- [Automating the Design of Embodied Agent Architectures](https://mlsanity.com/paper/2606.30111/) (2026)
- [FlowEvo: Self-Evolving Agents through the Co-Evolution of Workflows and Executable Skills](https://mlsanity.com/paper/2607.21596/) (2026)
- [Coupled Hierarchical Search over Topology and Execution for Agentic Workflow Synthesis](https://mlsanity.com/paper/2607.21609/) (2026)
- [Generating Workflow DAGs from Natural Language with Non-Reasoning LLMs](https://mlsanity.com/paper/2608.30250/) (2026)
- [Robust Workflow Generation via Adversarial Learning for Audio Deepfake Detection](https://mlsanity.com/paper/2609.20063/) (2026)
- [MoFlow: Multi-Objective Agentic Workflow Generation](https://mlsanity.com/paper/2609.38294/) (2026)
- [CB-Orchestrator: Adaptive Workflow Optimization for LLM Agents via Contextual Bandits](https://mlsanity.com/paper/2610.13064/) (2026)

## Abstract (arXiv)

The automated generation of agentic workflows is a promising frontier for enabling large language models (LLMs) to solve complex tasks. However, the empirical study reveals that existing agentic workflow generation methods are not robust. They often generate inconsistent workflows when provided with instructions that are semantically equivalent but phrased differently. This brittleness severely undermines their reliability in real-world applications. To tackle this challenge, we propose RobustFlow, a robust agentic workflow generation system that leverages preference optimization to learn invariance across instruction variations. We also introduce a benchmark of semantically equivalent instruction variants with node-level and graph-level metrics for evaluating workflow generation robustness. By training on these instruction variants, RobustFlow achieves workflow generation robustness scores of 70%--90% across diverse perturbations, outperforming existing approaches including AFlow and ScoreFlow. The code is publicly available at https://github.com/DEFENSE-SEU/RobustFlow.