GUIDE: Resolving domain bias in GUI agents with web video and annotation

Agents based on language and vision models (LVLM) have demonstrated an impressive ability to interact with graphical user interfaces (GUI). However, a recurring problem is domain bias: these agents perform well in generic environments but fail in specialized applications due to a lack of exposure to specific workflows and UI designs. The solution presented by GUIDE (GUI Unbiasing via Instructional-Video Driven Expertise) proposes a plug-and-play framework that eliminates the need to retrain models, extracting expert knowledge directly from web tutorial videos. This innovative approach uses an automated annotation pipeline based on inverse dynamics and a Video-RAG retrieval system powered by subtitles, which classifies, extracts, and matches relevant content to inject planning and positioning into the agent. Results on OSWorld show consistent improvements above 5% without modifying parameters or architectures, making it a model-agnostic enhancement. For companies looking to implement AI for businesses in an agile way, this type of advancement underscores the importance of having flexible systems that adapt to vertical contexts without large investments in retraining. At Q2BSTUDIO, as a software and technology development company, we offer customized solutions — from custom applications to infrastructure in cloud services aws and azure — that allow integrating AI agents with cybersecurity and business intelligence services such as Power BI. The combination of AI agents with automated annotation and knowledge retrieval opens new possibilities for automating complex processes, reducing execution steps and improving accuracy. This paradigm reinforces the need for a strategy that includes custom software and robust data pipelines, something that at Q2BSTUDIO we address with a comprehensive approach that spans from consulting to implementation.