Toward Embedded Intelligence: Architecting CPUs with PC AI Agents

Abstract This paper proposes a radical computing paradigm: integrating autonomous AI agents within the CPU system through coprocessor-based designs, where dedicated neural engines and context buffers support modular cognitive routines. Unlike traditional systems that rely on external software layers or cloud-based inference engines, the model brings cognitive capabilities closer to the hardware, reducing latency, power consumption, and contextual isolation. We explore the theoretical foundations, architectural implications, potential applications, and practical challenges of these designs, arguing that they represent the next evolutionary step in personal and distributed intelligence. Drawing on advancements in neuromorphic computing and embed- ded AI, we delineate how these agents achieve agency through self-contained reasoning modules, distinguishing them from conventional accelerators. Experimental simulations suggest up to 62% latency reduction and 77% energy savings in real-time tasks, validating the efficiency of event-driven neuromorphic processing. Key challenges—including security vulnerabilities, privacy, and ethical integration—are analyzed with reference to ongoing research. This framework shifts computing from passive execution to active cognition, fostering symbiotic human-machine partnerships.