Running the Gauntlet: Hard Agentic Tasks
cs.LG
Submitted: 2026-06-12
Updated: 2026-09-28
Code: https://github.com/sharpie7/circuitjs1
Project page: https://blog
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
The gist: As agentic systems continue to evolve and are widely deployed in real-world scenarios, there is a growing demand to faithfully evaluate their capabilities.
Terminology
Abstract
As agentic systems continue to evolve and are widely deployed in real-world scenarios, there is a growing demand to faithfully evaluate their capabilities. However, current benchmarks are typically built on popular applications with relatively simple tasks and focus on a narrow set of capabilities while overlooking broader dimensions, resulting in saturated performance on modern agents and failing to probe their limitations. To this end, we introduce GauntletBench, a web-based benchmark for evaluating agent generalisation in challenging scenarios, focusing on three underexplored capabilities (temporal perception, graphical understanding, and 3D reasoning), across five less-covered professional applications (Video Editor, Workflow Builder, 3D Modeller, Flight Analyser, and Circuit Designer), each with 27 vision-intensive tasks (135 in total). Our benchmark provides a modular pipeline that comprises an environment compatible with both open- and closed-source agent frameworks, a controlled web-based application, a well-structured task suite, and an automated evaluation engine with diverse metrics. Contrary to widespread expectations, our empirical results reveal that frontier agentic systems remain far from achieving human-level performance. Even the state-of-the-art agent achieves only a 28.2% success rate on our GauntletBench, highlighting the limitations in these overlooked capabilities and generalisation. By comparison, non-expert human annotators achieve over 80% success on our challenging yet feasible tasks, revealing the substantial gap between current agent capabilities and those required for complex real-world scenarios.
Sources
- On the Opportunities and Risks of Foundation Models
- Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration
- EntWorld: A Holistic Environment and Benchmark for Verifiable Enterprise GUI Agents
- OpenAI GPT-5 System Card
- Continuous Benchmark Generation for Evaluating Enterprise-scale LLM Agents
- OdysseyBench: Evaluating LLM Agents on Long-Horizon Complex Office Application Workflows
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