[P02] Agent-Aware GPU Memory Management: Ownership Transfer Semantics for AI Agent Systems

When an AI agent terminates, what happens to its GPU memory? Current systems release resources back to a pool, forcing successors to reallocate and recompute from scratch. This document presents an alternative approach: ownership transfer semantics where GPU memory passes to a designated successor instead of returning to a pool, preserving computational state across agent lifecycle transitions. The system implements three cascade termination policies for hierarchical agent systems. TERMINATE_ALL executes depth-first subtree termination with pre-transfer resource handling, ensuring orderly shutdown. ORPHAN_CHILDREN reparents orphaned agents to their grandparent rather than a root process, preserving hierarchy locality. ADOPT_BY_PEER assigns orphans to the least-loaded sibling, enabling automatic load balancing across peer agents. Zero-copy GPU sharing allows multiple agents to access a single memory allocation through a shared access list. Memory consumption reduces by 75% for four agents or 90% for ten agents sharing model weights. Critically, these sharing relationships survive ownership transfer, preserving team access continuity when coordinators change. This work addresses a gap in current GPU virtualization and process supervision systems, which lack agent-aware resource lifecycle management.