How do you deploy an AI agent on-premises?

First decide which parts must stay inside your network: the data, the model, the control layer, or all three. Host those on your own infrastructure, connect the agent to your identity system, keep the decision record on storage you control, and re-test every model or software update. On-premises moves work from a provider to you.

Why deploy on-premises at all?

Usually for one of four reasons: data that must not leave your environment, a supervisor or customer contract that expects it, systems that cannot be reached from outside, or a need to run without an internet connection. Name the reason before you start, because it decides how much has to move.

A common mistake is assuming everything must be on-premises when only the data does. If the concern is customer records leaving your network, you may be able to keep sources, records and enforcement inside while using a privately connected model endpoint. If the concern is that no data may leave at all, the model has to be inside too.

Hosting location does not change whether the agent is an AI system for regulatory purposes, or what your duties as a deployer are; see what counts as an AI system when you deploy an agent.

What does each component need?

What are the practical steps?

1. Write down the reason and the boundary. Which data and which components must stay inside, and why. Owner: risk with security.
2. Choose the hosting pattern. All inside, or data and control inside with a private model endpoint. Owner: technology.
3. Size the model hosting for peaks, not averages. A month-end or a service outage can multiply volumes.
4. Install the control layer inside your network, between the agent and every system it acts on.
5. Connect identity. Create the agent's own identity in your access management system; broker credentials per task.
6. Set up the record store. Append-only, backed up, with retention matched to your obligations.
7. Test on your own cases, on the exact model version you will run.
8. Run in shadow on live work, then start drafting at a low depth.
9. Plan updates. Every new model version is tested on the same cases before it replaces the old one.

What does this look like in practice?

Take a central securities depository whose systems are on an isolated network by design. It wants an agent to check incoming corporate action notices against its records and draft the announcements to participants.

Nothing can leave the network, so the model, the control layer, the sources and the record all run inside. The depository picks a model it can host on its own hardware, pins the version, and sizes capacity for its busiest season of notices. The agent's identity is created in its internal directory. Testing on two years of past notices finds the agent weak on notices with multiple options for holders; a specific procedure document is added as a source and the agent is re-tested.

Six months later a newer model version is available. It is run against the same case set, two regressions are found and fixed in the brief, and only then is it switched in. The general stages are the same as any deployment, set out in how to deploy an AI agent in a business.

Which controls matter most on-premises?

• Version pinning. The model you tested is the model that runs.
• Re-certification on every update, because nobody else is checking for you.
• Capacity monitoring, so peak volumes do not cause timeouts and retries.
• Tamper-evident records on storage your administrators cannot quietly edit.
• Patch discipline for the model host and control layer like any other critical system.
• Vendor assessment where you run vendor software inside; see assessing a vendor's AI agent before you deploy it.

If you are also regulated, the evidence demands rise; see deploying AI agents in a regulated business. The stages themselves are those in deploying an AI agent: train, shadow, certify, widen.