mlsanityData to 7 Oct 2026

Web Agents Should Use Typed Actions Instead of Click-Based Browsing

arXiv 2602.17245 ↗First posted 19 Feb 2026cs.AIcited by 0

On the map: LLM agents and tool use → Web, search, and research agents

Abstract (arXiv)

This position paper argues that building a reliable agentic Web requires shifting from low-level interaction primitives to typed actions supported by a semantic layer. Today's web agents primarily operate through clicks, keystrokes, and DOM manipulation, which leads to brittle long-horizon behavior, high execution cost, and limited auditability. We propose web verbs as a concrete design for this layer. A verb exposes a web operation as a typed function with structured inputs, structured outputs, and documented behavior, whether it is backed by a server-side Web API or a maintained client-side workflow. Verb calls can carry preconditions, postconditions, policy tags, and logging hooks, allowing agents to synthesize concise programs with explicit control flow and data flow and to produce checkable execution traces. Using representative case studies, we illustrate how verb-level composition can produce correct, reproducible outcomes, while browser agents using low-level interaction primitives may produce brittle behavior or incorrect reasoning. We conclude with a call to action on standardization, developer tooling, and community processes needed to make this semantic layer deployable and trustworthy at web scale.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 24 of its 36 references that are in the map. First posted on arXiv 19 Feb 2026.

Builds on

24 of 36 references.

Closest ancestors · 9

Same direction, newest first

  1. 2025WAREX: Web Agent Reliability Evaluation on Existing Benchmarks28 Sep
  2. 2025A Survey of WebAgents: Towards Next-Generation AI Agents for Web Automation with Large Foundation Models30 Mar
  3. 2024The BrowserGym Ecosystem for Web Agent Research6 Dec
  4. 2024Beyond Browsing: API-Based Web Agents21 Oct
  5. 2024AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents17 Oct
  6. 2024AutoWebGLM: A Large Language Model-based Web Navigating Agent4 Apr
  7. 2023WebArena: A Realistic Web Environment for Building Autonomous Agents25 Jul
  8. 2023Mind2Web: Towards a Generalist Agent for the Web9 Jun
  9. 2022WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents4 Jul

Other references · 15

  1. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
  2. 2023AgentBench: Evaluating LLMs as Agentscited by 1,211
  3. 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,134
  4. 2025UI-TARS: Pioneering Automated GUI Interaction with Native Agentscited by 584
  5. 2025MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agentscited by 226
  6. 2024Agent S: An Open Agentic Framework that Uses Computers Like a Humancited by 182
  7. 2025Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agentscited by 156
  8. 2025ComputerRL: Scaling End-to-End Online Reinforcement Learning for Computer Use Agentscited by 49
  9. 2025Towards Enterprise-Ready Computer Using Generalist Agentcited by 31
  10. 2025UltraCUA: A Foundation Model for Computer Use Agents with Hybrid Actioncited by 30
  11. 2024Ponder & Press: Advancing Visual GUI Agent towards General Computer Controlcited by 19
  12. 2025MGA: Memory-Driven GUI Agent for Observation-Centric Interactioncited by 18
3 more
  1. 2025ReCAP: Recursive Context-Aware Reasoning and Planning for Large Language Model Agentscited by 13
  2. 2025AgentProg: Empowering Long-Horizon GUI Agents with Program-Guided Context Managementcited by 10
  3. 2021Automatic Detection of Five API Documentation Smells: Practitioners' Perspectivescited by 6

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