The AI-Ready Enterprise Stack

Enterprise AI is usually discussed as if the model were the product. In practice, the model is just one layer in a much larger operating stack.

Foundation before autonomy

Identity sits at the base because agents cannot act safely without a clear access model. Which role is the agent assuming? What systems can it touch? Which actions can it take on behalf of a user, a team, or a process?

Governance comes next. Policy, approval paths, audit trails, and usage controls are not cleanup work after the system ships. They are part of what makes the system deployable in the first place.

Integration is the bridge into reality. MCP servers, APIs, event streams, and application connectors determine whether the agent sees fresh context and can do useful work without becoming an uncontrolled operator.

Platform and execution

Data platforms and AI platforms are often discussed together, but they solve different problems. The data layer provides context, memory, telemetry, and historical grounding. The AI platform provides model access, evals, prompt infrastructure, and guardrails.

Above that sits the runtime. This is where state, retries, branching, validation, and failure recovery live. Without a runtime, most agent systems are just prompt calls connected to hope.

At the top is what I think of as agent commerce: the point where AI begins participating in workflows, decisions, approvals, and transactions. That layer only works when everything underneath it is already holding.

The useful question for enterprise leaders is not whether they have an AI strategy. It is which layers of the stack are real, which are missing, and which are still being faked with manual intervention.