Generative AI has transformed human-computer interaction, yet most assistants remain reactive, relying on explicit prompts rather than understanding users’ evolving situations and needs. This talk presents our vision for always-on context engines: persistent intelligence layers that continuously perceive, interpret, and summarize multimodal signals from user interactions, applications, documents, communications, sensors, and surrounding environments to build actionable contextual knowledge. By maintaining an evolving understanding of activities, goals, preferences, social interactions, and environmental context, these systems enable assistants to provide timely, personalized, and proactive support while preserving continuity across interactions and devices.

We discuss the key building blocks of context engines, including:
- Multimodal perception and activity understanding
- Context acquisition, fusion, and state tracking
- Saliency estimation and context summarization
- Memory formation and personalization
- Intelligent triggering and proactive assistance
- Privacy-preserving and resource-efficient edge deployment

The talk discusses methods for identifying what information should be retained, when assistance should be offered, and how contextual knowledge can evolve without overwhelming users. It presents compact neural memory mechanisms that complement retrieval-based systems, enabling adaptation beyond conversational history while operating within the power, privacy, and compute constraints of edge devices It covers real-world applications of context-aware assistants and outline key research challenges in persistent context understanding, lifelong personalization, proactive assistance, privacy-preserving learning, and scalable deployment.