Running the Gauntlet: Re-evaluating the Capabilities of Agents Beyond Familiar Environments
Authors: name: Mykola Vysotskyi, name: Runqi Lin, name: Grzegorz Biziel, name: Michal Zakrzewski, name: Sebastian Montagna, name: Damian Rynczak, name: Shreyansh Padarha, name: Kumail Alhamoud, name: Zihao Fu, name: William Lugoloobi, name: Kai Rawal, name: Hanna Yershova, name: Xander Davies, name: Taras Rumezhak, name: Guohao Li, name: Fazl Barez, name: Baoyuan Wu, name: Arkadiusz Drohomirecki, name: Yarin Gal, name: Chris Russell, name: Christopher Summerfield, name: Adam Mahdi, name: Volodymyr Karpiv, name: Philip Torr, name: Adel Bibi
Organization: University of Oxford

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 20 vision-intensive tasks (100 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 19.1% 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.