Beyond BrowseComp: Why VeriGUI Excels in Fine-Grained Evaluation

Why BrowseComp Matters

Released by OpenAI, BrowseComp is designed to measure how well AI agents can navigate the web and extract answers to hard-to-find questions. The benchmark includes over a thousand fact-seeking prompts that cannot be solved by surface-level lookups.

Its key strength lies in factual retrieval: agents are tested on persistence, reasoning over multiple sources, and producing verifiable answers. This makes BrowseComp a solid tool for evaluating search-heavy use cases such as browsing assistants or fact-checking agents.

However, the limitation is clear: BrowseComp looks only at the final answer. It cannot capture whether the agent followed an efficient process, handled a sequence of subtasks correctly, or adapted to changing contexts.

BrowseComp - Browsing Agents

Where VeriGUI Stands Out

VeriGUI was designed to fill BrowseComp’s gap. Instead of focusing solely on information retrieval, it recreates real-world workflows inside graphical interfaces.

• Fine-grained subtasks: Tasks are decomposed into multiple dependent steps, often expanding into hundreds of individual GUI actions.
• Step-level verification: Each subtask has its own ground truth, so failures are diagnosed precisely rather than being hidden in the final output.
• Closer to reality: Many practical use cases — filling forms, managing applications, or navigating enterprise tools — require reliable multi-step planning. VeriGUI mirrors this complexity far better than web-only benchmarks.

This approach makes VeriGUI a valuable complement to BrowseComp: while BrowseComp shows whether an agent can dig up facts, VeriGUI shows whether it can reliably execute long, interactive sequences without breaking down.

VeriGUI: Real-World GUI Trajectories for Rigorous Agent Testing

BrowseComp vs VeriGUI: Finding the Right Fit

Both benchmarks offer value, but their depth differs significantly:

• If your agent is meant to search the web, reason across documents, and return factual answers, then BrowseComp is an effective measure of progress.
• If your focus is on autonomy in software, user interface navigation, and step-by-step execution, VeriGUI provides richer insights into performance.

Used together, they provide a fuller picture: BrowseComp highlights search and reasoning ability, while VeriGUI exposes workflow robustness and planning skills. In practice, BrowseComp can serve as a useful baseline for web-centric performance, yet VeriGUI provides the more comprehensive and forward-looking assessment

At ABAKA AI, we specialize in creating the kind of datasets and evaluation frameworks that benchmarks like VeriGUI are built on. From multimodal data collection to fine-grained annotation, our work supports companies and researchers in making agents more reliable and effective. If your team is exploring ways to strengthen data pipelines or benchmarking strategies, contact us.