Your next integration could begin with an agent reading your documentation and end with an application calling your API every day. That’s an opportunity I think infrastructure teams should be testing.
Imagine you run a scheduling service. You’ve dealt with time zones, concurrent reservations, cancellations and retries that accidentally create duplicate bookings.
Then a salon owner opens an AI builder and types:
“Build me a website where customers can choose a service, book an appointment and get a confirmation email.”
Underneath that interface, the agent needs a pipeline: check availability, reserve a slot, confirm the booking and trigger a notification. It could integrate a service, install a library or generate its own implementation.
If it integrates your service, there are two consumers to think about. The agent selects and connects the infrastructure. The application uses it in production. The team operating that application controls the budget and permissions.
In Amplifying’s February 2026 benchmark, 2,430 Claude Code responses showed strong preferences for particular tools alongside custom implementations. The study measures choices within specific models and projects. Whether changes to your own service influence those choices still needs testing.
Now imagine the agent finds your service, but the examples are outdated, authentication is poorly documented and getting a sandbox requires a sales call. Each of those is another obstacle between finding your infrastructure and completing an integration.
Make the integration work
I’d start by making one workflow work from the published documentation.
For our scheduling service, that could mean GET /availability and POST /reservations, described in an OpenAPI specification with explicit request and response schemas. Include realistic sandbox data and a clear way to obtain credentials with limited permissions.
Then test whether the agent can use that contract.
It should be able to read the availability response, construct a valid reservation request and understand the result. If another request takes the slot first, the service could return slot_unavailable. The documented recovery path should explain how to refresh availability and offer another slot.
That gives you something observable: whether the agent can handle the failure without a developer explaining what to do next.
The service still has to enforce the underlying guarantees. Reserving a slot must prevent conflicting bookings. Retrying the same operation with the same idempotency key should return the documented result without creating a second reservation. Stripe’s idempotency documentation is a concrete example of that contract.
A reservation.confirmed event could feed the notification service. Document delivery retries and event IDs so downstream consumers can handle duplicates, as illustrated in Stripe’s webhook guidance.
An MCP server can expose relevant operations to connected, compatible agents through tool descriptions and input schemas. The MCP documentation explains that mechanism.
In this example, the coding agent could use MCP during setup and testing, while the finished application uses HTTP and events in production. Both interfaces should reach the same business rules.
What teams are paying for
The paid value is the scheduling capability you continue operating: concurrency handling, monitoring, recovery and maintenance. Make the billing unit explicit, including how retries affect usage charges. That gives the team a service it can evaluate on reliability, operational effort and cost.
For me, “AI native” earns its meaning when an agent can complete that integration and the resulting application behaves reliably.
Measure selection, then production use
Run the salon task with your integration available. Measure successful completion, manual interventions and recovery from failures. Separately, run it without naming or preinstalling your service and record whether it gets considered.
Those tests answer different questions: can an agent use your infrastructure, and does it choose to?
Then follow the integrations into production. Continued usage and paid retention are the evidence that the service is worth operating as a business.
I’d start with one capability and one complete pipeline. Make it understandable, test it with agents and see whether the resulting applications keep using it. That’s a practical way to find out where your infrastructure belongs in this market.