ClawBench: Can AI Agents Complete Everyday Online Tasks?

• URL: http://arxiv.org/abs/2604.08523v1
• Date: Thu, 09 Apr 2026 17:57:13 GMT
• Title: ClawBench: Can AI Agents Complete Everyday Online Tasks?
• Abstract summary: ClawBench is an evaluation framework of 153 simple tasks that people need to accomplish regularly in their lives and work.<n>ClawBench operates on production websites, preserving the full complexity, dynamic nature, and challenges of real-world web interaction.<n>A lightweight interception layer captures and blocks only the final submission request, ensuring safe evaluation without real-world side effects.
• Score: 50.958690494341106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: AI agents may be able to automate your inbox, but can they automate other routine aspects of your life? Everyday online tasks offer a realistic yet unsolved testbed for evaluating the next generation of AI agents. To this end, we introduce ClawBench, an evaluation framework of 153 simple tasks that people need to accomplish regularly in their lives and work, spanning 144 live platforms across 15 categories, from completing purchases and booking appointments to submitting job applications. These tasks require demanding capabilities beyond existing benchmarks, such as obtaining relevant information from user-provided documents, navigating multi-step workflows across diverse platforms, and write-heavy operations like filling in many detailed forms correctly. Unlike existing benchmarks that evaluate agents in offline sandboxes with static pages, ClawBench operates on production websites, preserving the full complexity, dynamic nature, and challenges of real-world web interaction. A lightweight interception layer captures and blocks only the final submission request, ensuring safe evaluation without real-world side effects. Our evaluations of 7 frontier models show that both proprietary and open-source models can complete only a small portion of these tasks. For example, Claude Sonnet 4.6 achieves only 33.3%. Progress on ClawBench brings us closer to AI agents that can function as reliable general-purpose assistants.

Related papers

• Running the Gauntlet: Re-evaluating the Capabilities of Agents Beyond Familiar Environments [67.2234316079859]
  GauntletBench is a web-based benchmark for evaluating agent generalisation in challenging scenarios.<n>It focuses on three underexplored capabilities (temporal perception, graphical understanding, and 3D reasoning) across five less-covered professional applications.<n>Our empirical results reveal that frontier agentic systems remain far from achieving human-level performance.
  arXiv  Detail & Related papers  (2026-06-12T12:32:24Z)
• AIRS-Bench: a Suite of Tasks for Frontier AI Research Science Agents [49.67355440164857]
  We introduce AIRS-Bench, a suite of 20 tasks sourced from state-of-the-art machine learning papers.<n>Airs-Bench tasks assess agentic capabilities over the full research lifecycle.<n>We open-source the AIRS-Bench task definitions and evaluation code to catalyze further development in autonomous scientific research.
  arXiv  Detail & Related papers  (2026-02-06T16:45:02Z)
• Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces [126.23612941699565]
  Terminal-Bench 2.0 is a benchmark composed of 89 tasks in computer terminal environments inspired by problems from real world.<n>We show that frontier models and agents score less than 65% on the benchmark.<n>We publish the dataset and evaluation harness to assist developers and researchers in future work at https://www.tbench.ai/.
  arXiv  Detail & Related papers  (2026-01-17T01:29:30Z)
• FieldWorkArena: Agentic AI Benchmark for Real Field Work Tasks [52.47895046206854]
  FieldWorkArena is a benchmark for agentic AI targeting real-world field work.<n>This paper defines a new action space that agentic AI should possess for real world work environment benchmarks.
  arXiv  Detail & Related papers  (2025-05-26T08:21:46Z)
• PaperBench: Evaluating AI's Ability to Replicate AI Research [3.4567792239799133]
  PaperBench is a benchmark evaluating the ability of AI agents to replicate state-of-the-art AI research.<n>Agents must replicate 20 ICML 2024 Spotlight and Oral papers from scratch.<n>PaperBench contains 8,316 individually gradable tasks.
  arXiv  Detail & Related papers  (2025-04-02T15:55:24Z)
• TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks [55.03911355902567]
  We introduce TheAgentCompany, a benchmark for evaluating AI agents that interact with the world in similar ways to those of a digital worker.<n>We find that the most competitive agent can complete 30% of tasks autonomously.<n>This paints a nuanced picture on task automation with simulating LM agents in a setting a real workplace.
  arXiv  Detail & Related papers  (2024-12-18T18:55:40Z)
• AutoPenBench: Benchmarking Generative Agents for Penetration Testing [42.681170697805726]
  This paper introduces AutoPenBench, an open benchmark for evaluating generative agents in automated penetration testing. We present a comprehensive framework that includes 33 tasks, each representing a vulnerable system that the agent has to attack. We show the benefits of AutoPenBench by testing two agent architectures: a fully autonomous and a semi-autonomous supporting human interaction.
  arXiv  Detail & Related papers  (2024-10-04T08:24:15Z)
• Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models [33.1538965735133]
  Cybench is a framework for specifying cybersecurity tasks and evaluating agents on those tasks.<n>We include 40 professional-level Capture the Flag (CTF) tasks from 4 distinct CTF competitions.<n>We construct a cybersecurity agent and evaluate 8 models: GPT-4o, OpenAI o1-preview, Claude 3 Opus, Claude 3.5 Sonnet, Mixtral 8x22b Instruct, Gemini 1.5 Pro, Llama 3 70B Chat, and Llama 3.1 405B Instruct.
  arXiv  Detail & Related papers  (2024-08-15T17:23:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.