Architecting the Agentic AI Systems Stack What Should Infrastructure Manage When the Unit of Work Is a Trajectory-
Agentic AI is becoming a systems workload. A delegated goal is not a single prompt-response request; it unfolds as an agentic trajectory through model calls, tool calls, context operations, waits, approvals, recovery actions, and external effects. The trajectory is the smallest unit over which budget, authority, recovery, evidence, and physical cost can be stated without hiding essential state in application convention. Existing managed units still matter. Instructions, processes, requests, and model calls each made a different layer of the stack schedulable, protectable, accountable, recoverable, and optimizable. Agentic trajectories strain these units because state, authority, cost, and side effects span many calls. The stack therefore needs a system-visible unit for such trajectories. This paper calls that unit a managed trajectory and focuses on the contract that gives lower layers a common handle on identity, goals, budgets, placement, memory, authority, effects, evidence, and recovery. From a computer architect's perspective, agentic work is an interface and resource-management problem. The paper develops a stack organization and open research agenda around context hierarchy, protected effects, and metrics for useful work per verified action rather than only tokens per second.