This topic covers AI systems that learn to perform complex computer and UI tasks by watching screen recordings or demonstration videos—exemplified by models like FDM-1 and a growing set of rivals. These video-to-action systems combine video-language understanding, imitation learning and program synthesis to convert visual demonstrations into reproducible steps, scripts or agent policies for automation, testing and developer assistance. The topic is timely in 2026 because advances in multimodal foundation models, improved video datasets, and production-ready agent frameworks have made video-driven automation practical for many software workflows. Organizations are adopting these capabilities for GUI automation, end-to-end test generation, onboarding scripted workflows, and agentic developer tooling, while facing new evaluation, reliability and safety challenges. Key tooling and categories: AI Automation Platforms and GenAI Test Automation use video-learned behaviors to drive UI workflows and generate tests—Qodo focuses on quality-first test generation and SDLC governance, while platform builders (LangChain, MindStudio) provide the engineering and low-code/no-code frameworks to compose, evaluate and deploy agentic applications that operationalize video-derived policies. AI-native and online IDEs (Windsurf, Replit) and developer assistants (GitHub Copilot, Aider) are the integration points where video-to-code or video-to-action outputs become part of the developer loop, enabling live previews, context-aware edits, and deployment. Practical adoption hinges on rigorous test harnesses, reproducible benchmarks, privacy-aware training data, and mechanisms to validate robustness across UI variants. For teams evaluating FDM-1–style systems, balance potential automation gains with requirements for continuous evaluation, governance, and secure handling of sensitive screen content.