Intent-aware, vision-powered desktop assistants and smart-cursor tools pair on-screen computer vision with multimodal language models and agentic automation to understand what a user is doing and take or suggest context-sensitive actions. These systems observe window contents, UI elements and user gestures at the edge, infer intent, and either offer inline suggestions (e.g., copy-and-paste transforms, calendar actions, or code fixes) or execute multi-step workflows across apps. The topic is timely as of 2026‑05‑13 because advances in multimodal models (e.g., Google Gemini, Anthropic’s Claude family), agentic interfaces (Adept’s ACT-1), and managed ML platforms (Vertex AI) make accurate intent inference and safe action-taking feasible. Enterprise platforms such as Kore.ai and IBM watsonx Assistant focus on orchestration, governance and observability for multi-agent flows, while productivity-integrated assistants like Microsoft 365 Copilot and app-native AI in Notion show how contextual assistance improves real workstreams. Edge-first deployment and on-device vision lower latency and help address privacy and compliance requirements for sensitive screens. Key technical and product themes include: on-device or hybrid vision processing to recognize UI affordances; multimodal LLMs for intent interpretation and natural-language interaction; agentic execution engines that manipulate software interfaces; and governance/monitoring layers for auditability. Practical considerations include accessibility, minimizing false activations, data residency, and secure UI automation. Together these capabilities enable smarter cursors and desktop agents that speed routine tasks while raising legal, security and UX design questions that organizations must address when deploying them at scale.