AI agents are becoming useful enough to do real work.
They can answer customer questions, retrieve orders, investigate support issues, and interact with the systems businesses already depend on.
But every new agent introduces a question that is easy to overlook when you're focused on getting the first demo working:
How much access should this agent actually have?
If an agent needs to look up an order, should it have direct database access? If it needs information from your CRM, should it hold a copy of your CRM's API key? And if five different agents interact with the same systems, how do you know which one did what?
These aren't theoretical concerns. They're the practical questions that determine whether an organization can move beyond AI experiments and start trusting agents with real business workflows.
That's the problem behind the Xano Intelligence Layer.
We've published a working template that demonstrates how to give AI agents a single, governed way to access business data and capabilities, without distributing credentials and access rules across every agent.
The problem isn't connecting AI agents to data
Connecting an AI agent to an API is relatively straightforward. You give it a tool, configure credentials, and let it make requests.
That works well enough when you're experimenting with one agent and one system.
But imagine a company introducing several agents:
- A sales assistant that retrieves orders and customer information.
- A support assistant that looks up accounts and opens tickets.
- An operations agent that checks inventory and delivery conditions.
- A third-party agent that needs access to one specific workflow.
Each needs different permissions. Some should only read data. Others may be authorized to perform specific actions.
The easiest implementation is often to give every agent the credentials it needs and handle permissions separately.
The result can become difficult to govern: credentials spread across tools, authorization logic implemented inconsistently, and no unified record of what agents attempted or completed.
The challenge isn't making the connections work. It's making those connections manageable as the number of agents grows.
One governed interface between your agents and your data
The Xano Intelligence Layer introduces a different architecture.
Instead of connecting each agent directly to your underlying systems, you put a governed interface between them.
An agent doesn't need to know where an order database lives or which credential is required to access it. It needs to know that it can call an approved capability such as orders.search.
The Intelligence Layer handles the rest.
When an agent makes a request, the layer:
- Identifies the caller.
- Looks up the requested capability.
- Checks whether the caller has permission.
- Routes authorized requests to the correct system.
- Records the outcome in an audit log.
That means an agent can be allowed to search orders without being allowed to modify them. It also means a denied request is visible rather than silently disappearing.
And because the downstream credentials stay behind the layer, you don't have to distribute them to every AI client.
Three controls that make AI agent governance practical
Permissions: Give each agent only the capabilities it needs, and reject unauthorized or unknown operations before they reach business systems.
Credential isolation: Keep database and downstream API credentials in the governed backend instead of distributing them to every agent.
Auditability: Record successful, denied, and failed requests so operators can understand which agent attempted which action and what happened.
Together, these controls make agent integrations easier to inspect, change, and operate as adoption grows.
The capability registry is the foundation
One of the most useful parts of this architecture is the capability registry.
Instead of hardcoding every integration into each agent, the registry defines the operations available through the Intelligence Layer.
A capability can specify the operation, its destination, the permission required, and how the request should be routed.
For example:
- orders.search: Search existing orders.
- clients.get: Retrieve permitted customer information.
- tickets.create: Create a support ticket.
An agent receives scopes corresponding to the capabilities it needs.
If the support agent needs to search orders and create tickets, it can receive those two permissions. If the sales agent only needs to search orders, it doesn't need ticket-creation access.
The registry also acts as an allow-list. A request for a capability that hasn't been registered is rejected rather than forwarded.
This becomes particularly valuable when an organization is connecting multiple agents to multiple systems.
The same controls apply whether the request comes from an API or an MCP client
Model Context Protocol is making it easier for AI tools to interact with external systems. But the more tools an agent can discover, the more important it becomes to control what those tools can actually do.
The Intelligence Layer template includes an MCP server that exposes four governed tools:
- List available capabilities.
- Resolve an intent to a capability.
- Call a capability.
- Read recent audit records.
Rather than exposing the underlying business endpoints directly, the MCP server sends requests through the same permission-checking and auditing path used by the gateway's APIs.
That gives developers a consistent approach whether a request originates from Claude, Cursor, a custom agent, or a direct API integration.
MCP provides the connection mechanism. The Intelligence Layer provides the governed access path behind it.
What makes this more than an architecture diagram?
We wanted the template to be something developers could actually inspect and build on.
The Xano Intelligence Layer template includes:
A working backend. The template contains 11 database tables, 27 APIs, and 18 functions, including the capability registry, agent identities, request routing, and audit logging.
An AI-powered router. A built-in model can select tools for requests. The template also includes keyword-based capability resolution that works without an LLM.
A reference data source. A fictional paper distributor provides sample orders, customers, branches, and sales representatives so developers can test the architecture without connecting sensitive company data.
External API examples. The template demonstrates how governed requests can reach services beyond the Xano workspace.
An MCP interface. Developers can connect supported MCP clients through the gateway instead of exposing the underlying data endpoints.
Workflow tests. Included tests cover permission denials, unknown capabilities, key revocation, request auditing, and upstream failures.
The backend is implemented in XanoScript, so developers can inspect and extend the logic rather than treating the gateway as an opaque service.
From a working demonstration to a real deployment
The template is designed to demonstrate the architecture, not to imply that a fresh installation is ready to handle sensitive production data.
There are important steps before using it with real systems.
The template ships with sample security values, and some installation paths leave security variables unset. Those settings must be replaced and enforced before production use.
Developers should also assign narrow permissions to each agent, configure appropriate credentials for each downstream system, and verify that the health checks report secure configuration.
For a production deployment, organizations should consider additional safeguards such as rate limiting, key expiration, audit-data retention, and redaction.
These details matter because a governed access layer is only as trustworthy as its configuration.
The advantage of the architecture is that these controls have a clear place to live.
Why this matters for AI-built software
We're seeing a shift in what developers need from AI infrastructure.
Generating an application or connecting an agent to a tool is increasingly fast.
The harder work begins when that software needs to interact with real business systems, enforce permissions, survive changes, and provide enough visibility for teams to operate it confidently.
The same principle applies whether you're building an AI-generated application or deploying a fleet of AI agents.
The ability to generate software doesn't eliminate the need to govern what that software can do.
That's where Xano fits.
Xano provides a backend where APIs, data, business logic, and integrations can be built, inspected, tested, and operated together.
The Intelligence Layer template shows how those capabilities can be used to create a governed interface for AI agents.
Instead of treating every new agent as another direct connection to your systems, you can give agents a common way to request approved capabilities while retaining control over access and visibility into what happens.
Build your own Intelligence Layer
If you're experimenting with agents that need access to business data, the template is a practical place to start.
You can explore the architecture, run it with the included sample data, inspect how permissions are enforced, and adapt the capabilities to your own systems.
Explore the Xano Intelligence Layer template
Watch the Intelligence Layer walkthrough
Start with a working agent. Build the controls that let you trust it.
