Glossary
Unified terminology is the first step toward organizational consensus.
Conclusion first: in the context of AI-native infrastructure, key terms must remain consistent; otherwise, both governance and communication lose focus.
The following glossary serves to align cross-team terminology.
Core Terms
AI Native Infrastructure
An infrastructure system premised on “models/agents as execution entities, compute as scarce assets, and uncertainty as the norm,” closed-looping “intent → execution → resource consumption → economic and risk outcomes” through compute governance.
Model-as-Actor
Models/agents become “execution entities” with action capabilities, capable of invoking tools, modifying system state, and producing side effects, thus requiring governance and audit.
Compute-as-Scarcity
Compute (GPU, interconnects, power consumption, bandwidth) becomes the core scarce asset, with expansion constrained by supply chain and data center conditions, and costs that cannot be made elastic.
Uncertainty-by-Default
Behavior and resource consumption are highly uncertain (especially in agentic and long-context scenarios), requiring verification and fallback mechanisms.
Intent Plane
The API, Agent, and policy expression layer responsible for expressing “what I want,” including priorities, budgets, compliance, and other policies.
Execution Plane
The training/inference/serving/runtime layer responsible for translating intent into actual execution, including state management, tool invocation, model routing, and so on.
Governance Plane
The quota/budget, isolation/sharing, and cost control layer responsible for bounding resource consequences, including topology-aware scheduling, SLO and risk policies.
The Loop
Possessing a closed loop of “intent → consumption → cost/risk outcomes,” comprising four steps: Admission, Translation, Metering, and Enforcement.
Compute Governance
Governing the resource consequences of intent, including four categories of objects: token economics, accelerator time, interconnect and storage, and organizational budgets and risks.
FinOps / Financial Operations
Embedding cost governance early into architecture so that every scaling decision simultaneously answers “whether performance meets requirements” and “whether it is affordable.”
Agent
An execution entity that completes tasks by selecting tools, invoking tools, and iterating reasoning, with uncertain behavioral paths and resource consumption.
MCP / Model Context Protocol
A protocol that standardizes tool access as “declaratable capability boundaries,” defining how capabilities are exposed to models/agents and how they are invoked.
Operating Model
Institutional design for organization and operational methods, including responsibility boundaries, collaboration mechanisms, and decision-making processes, answering “who is responsible for what and what are the costs of failure.”