# ReUseIt: Synthesizing Reusable AI Agent Workflows for Web Automation

arXiv: https://arxiv.org/abs/2510.14308 · HTML: https://mlsanity.com/paper/2510.14308/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- First posted on arXiv 16 Oct 2025.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Built on by (most cited first, 6 of 6)

- [Agent Skills: A Data-Driven Analysis of Claude Skills for Extending Large Language Model Functionality](https://mlsanity.com/paper/2602.08004/) (2026)
- [Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learning](https://mlsanity.com/paper/2605.02913/) (2026)
- [Test-Time Learning with an Evolving Library](https://mlsanity.com/paper/2605.14477/) (2026)
- [SKILL.nb: Selective Formalization and Gated Execution for Durable Agent Workflows](https://mlsanity.com/paper/2606.08049/) (2026)
- [Motif: Discovering and Automating Personal Web Workflows](https://mlsanity.com/paper/2607.10531/) (2026)
- [Lost in Implicit Routing: How LLM Agents Fail to Load Relevant Skills at Scale](https://mlsanity.com/paper/2610.18522/) (2026)

## Abstract (arXiv)

AI-powered web agents have the potential to automate repetitive tasks, such as form filling, information retrieval, and scheduling, but they struggle to reliably execute these tasks without human intervention, requiring users to provide detailed guidance during every run. We address this limitation by automatically synthesizing reusable workflows from an agent's successful and failed attempts. These workflows incorporate execution guards that help agents detect and fix errors while keeping users informed of progress and issues. Our approach enables agents to successfully complete repetitive tasks of the same type with minimal user intervention, increasing the success rates from 24.2% to 70.1% across fifteen tasks. To evaluate this approach, we invited nine users and found that our agent helped them complete web tasks with a higher success rate and less guidance compared to two baseline methods, as well as allowed users to easily monitor agent behavior and understand its failures.