The history of information infrastructure can be read as a history of connectivity. Transmission Control Protocol/Internet Protocol (TCP/IP) made heterogeneous hosts reachable and the World Wide Web made documents linkable. A new connectivity problem is now emerging: The object to be connected is a capability-bearing agent that can perceive, reason, invoke tools, or act in the physical world. However, these capabilities remain locked inside platforms, organizations, and runtimes, while existing frameworks, interface protocols, and agent-interconnection efforts still leave open the question of what stable public abstraction can connect agents, humans, and infrastructure as a network architecture problem across organizational and physical domains. This vision and position paper argues for agent network: A reference architecture for capability discovery, connection, and coordination among heterogeneous, autonomous, and potentially self-interested agents. We do not present a complete protocol specification or a performance evaluation. Its central position is that open agent networks need a dual narrow-waist architecture. The Agent Locator Protocol (ALP), surfaced through agent://<name>, provides the connectivity waist for making capability-bearing participants globally locatable, reachable, and minimally deliverable across substrates and organizations. The Task Semantic Intermediate Representation (TSIR) protocol provides the semantic waist for circulating task intent and success criteria as signable and reusable objects. Around two waists, we propose the Agent Shared Cognition Protocol (ASCP) as a candidate coordination runtime for adjustable group cognition, with blackboard-like workspaces and sedimentation as reference mechanisms for shared working state and experience reuse. Together, these abstractions define Networked Intent Realization (NIR): A research agenda for preserving high-level intent, routing it to capable participants, and translating it into executable, traceable, and accountable collaboration across open agent networks.

Camera-equipped mobile devices are encouraging people to take more photos and the development and growth of social networks is making it increasingly popular to share photos online. When objects appear in overlapping Fields Of View (FOV), this means that they are drawing much attention and thus indicates their popularity. Successfully discovering and locating these objects can be very useful for many applications, such as criminal investigations, event summaries, and crowdsourcing-based Geographical Information Systems (GIS). Existing methods require either prior knowledge of the environment or intentional photographing. In this paper, we propose a seamless approach called “Spotlight”, which performs passive localization using crowdsourced photos. Using a graph-based model, we combine object images across multiple camera views. Within each set of combined object images, a photographing map is built on which object localization is performed using plane geometry. We evaluate the system’s localization accuracy using photos taken in various scenarios, with the results showing our approach to be effective for passive object localization and to achieve a high level of accuracy.