Your backend already knows how to do the work

Create an order. Update a customer. Book a delivery. Send an invoice. Your existing software already does all of this, through its screens and, usually, an API. What it doesn't have is a safe way for an AI agent to do it.

That's the missing piece in most companies' AI plans. Not a new app, but a layer that lets agents use the software you already pay for, without giving them the keys to everything.

What the agent layer is

In practice it's a small, well-defined service between AI agents and your systems:

  • Tools: the specific actions agents may take, defined with clear inputs and outputs, often exposed through MCP so any agent can use them.
  • A gateway: checks every call, validates inputs, enforces scoped permissions per agent and per user.
  • Approvals: anything that sends, pays, deletes, or commits waits for a human yes.
  • Adapters: translate between clean tools and your messy reality, like old APIs, databases, or even a browser for systems without an API.
  • Logs: every call, input, and result recorded, so you can see exactly what an agent did and why.

Why not let the agent call your APIs directly

It works in a demo. In production, direct access means an agent can do anything the API key can do, with inputs nobody validated, and no record of why. One confused step and it has emailed the wrong customer list or updated a thousand records.

The agent layer is how you get the speed of agents without that risk. It's also what makes switching models easy: the tools stay the same whether the agent is Claude today or something else next year.

What it looks like in a real company

For a freight company: an agent reads quote requests from email, checks rates through a tool, and drafts a quote that a planner approves. For a shop: an agent answers order questions using read-only tools, and hands anything unusual to a person. For a manufacturer: an agent pulls numbers from the ERP and the production database and writes the Monday report.

In each case, the existing systems are untouched. The agent only ever does what the layer allows.

From prototype to production

The first version is usually read-only: agents can look things up and draft, but not change anything. Once the logs show it's reliable, write actions are added one by one, each with approval at first. Costs and errors are tracked from day one, so it's clear what the agents actually save.

Where to start

List the five most repetitive things people do across your systems each week. One of them is almost always a good first agent. The scanner on the AI systems page will suggest ideas for your business from your website, if you want a starting point.