[P70] Agent Memory Controller: Hardware Architecture for GPU Memory Ownership Transfer in AI Agent Systems

When AI agents terminate, traditional memory management releases their GPU memory to a shared pool. This destroys data continuity. Successor agents lose access to model weights, attention caches, and inference results. They must re-initialize from scratch. For long-running AI workflows spanning multiple agent generations, this creates unacceptable overhead. This paper presents the Agent Memory Controller (AMC), a dedicated hardware circuit for GPU memory management with agent ownership semantics. The key architectural principle: memory regions TRANSFER to designated successors rather than RELEASE to pool upon agent termination. The AMC comprises five integrated subsystems: a content-addressable region table circuit, an ownership transfer circuit with sub-10-nanosecond completion, a cascade termination circuit supporting hierarchical agent relationships, a shared access controller for multi-agent memory sharing, and a hardware-enforced access control matrix. Performance specifications: single-clock-cycle ownership transfer without data movement, support for 4,096 concurrent memory regions, 10-level cascade depth for hierarchical termination, and single-cycle access validation. The system integrates with agent coordination engines, safety monitors, and audit units through dedicated hardware interfaces. Implementation targets include GDSII and OASIS integrated circuit layouts with content-addressable memory for region lookup.