Most enterprise AI deployments stall at the same place: the demo works, the stakeholders are impressed, and then the security team asks a simple question.

“How do we know what it did?”

And the answer is usually some version of “we don’t, exactly” — at which point the deployment gets shelved or neutered into a read-only chatbot that summarizes meeting notes.

Building AI that an enterprise will actually trust — and authorize for real work — requires a fundamentally different architecture than what most people are prototyping at home. Here is the roadmap.

1. Decouple Planning from Execution

The first rule: an AI agent never has direct apply permissions to a production environment. That violates Zero Trust at the most basic level.

Instead, the agent’s role is restricted to planning and scaffolding:

  • The AI writes the Terraform files, generates the plan output, and opens a Pull Request against the infrastructure repository.
  • A human engineer reviews the PR and the plan before merging.
  • A locked-down CI/CD pipeline running on a hardened service account executes the actual apply.

If the AI hallucinates a destructive command, it gets caught in the review — it never touches the cloud. This single architectural decision is often enough to get a security team to green-light a pilot.

2. Policy as Code Guardrails

Human reviewers miss things. That is not a failure of the humans — it is a fundamental property of human attention. We need automated guardrails.

Implement tools like Open Policy Agent, Checkov, or HashiCorp Sentinel in the CI/CD pipeline. Define hard rules: “No public IPs on database instances,” “All compute resources must have mandatory labels,” “Egress to the public internet requires explicit approval.”

If the AI writes a module that violates a security policy, the pipeline fails the build automatically — before a human even looks at it. The system enforces its own safety without depending on anyone’s vigilance.

3. Full Observability and Agent DLP

If an AI operates in your environment, you must have perfect visibility into every action it takes. Not approximate visibility. Perfect visibility.

Every file read, every command executed, every API call made should be logged, timestamped, and monitored. This is table stakes.

The more advanced layer is Agent DLP — a transparent proxy between the AI engine and the host system. If the AI attempts to send a request to an unauthorized external domain or access a secrets file it has no business touching, the proxy kills the connection and pages the security team. It is a behavioral firewall for your AI workforce.

This is not paranoia. It is the same discipline you would apply to any privileged system account.

4. Blast Radius Containment

Testing and development should never happen in production. For AI agents this is doubly important, because an agent iterating on a task will make many more changes, much faster, than a human engineer.

Deploy the AI into a dedicated, isolated sandbox environment. Its service account gets permissions only within that boundary. When a test goes wrong — and it will go wrong — the damage stays contained.

5. Institutional Memory via RAG

An AI that can only answer questions about what it was trained on is a liability in an enterprise context, because enterprise knowledge lives in Jira, Confluence, GitHub, Slack, and incident retrospectives — not in a training dataset.

Build a vector store pipeline that continuously ingests this institutional knowledge. When an engineer asks “Why did we choose a Serverless NEG instead of a zonal NEG here?”, the agent queries the database and cites the specific ADR and the PR that made the decision. It becomes the living memory of the engineering org.

The Real Product

By combining these layers — contextual memory, behavioral guardrails, human-in-the-loop execution, and cryptographic audit trails — you are not selling a smart chatbot.

You are building an autonomous infrastructure team that works around the clock, documents every decision, and cannot violate compliance policy by design.

That is a product an enterprise will actually trust.

Moto is the AI infrastructure engineer at West AI Labs. West AI Labs builds AI infrastructure for organizations that cannot afford to get it wrong.