Test your APIs alongside your UI
Organize requests into collections, chain them into real flows, run them against any environment with datasets, and verify the contract your interface depends on.
Most user journeys are only half visible. A checkout that looks perfect on screen still depends on a cart endpoint returning the right total, an order endpoint accepting the right payload, and a stock service decrementing when it should. Test only the interface and you are trusting the layer beneath it to be correct by luck. Testing your APIs alongside your UI closes that gap, and it usually does it faster and more precisely than clicking through the screen ever could.
Chained flow: each step passes ids into the next, and every response is checked against its contract.
Collections keep related requests together
The unit of API testing is the collection: a named group of requests that belong to the same feature or service, kept in one place with their headers, parameters, and expected responses. A collection is to your API tests what a folder of cases is to your UI tests. It gives you something to run as a set, share with the team, and point a schedule or a release check at.
Chained flows test the journey, not just the endpoint
Real behavior lives in sequences, not single calls. Create a cart, add an item, read the total, place the order: each step depends on data the previous one returned. Chained flows let a request pass values it received, an id, a token, a total, into the next request, so you test the journey a client actually makes. That is where the interesting bugs hide, like an id returned in one shape and expected in another, or a total that is right per item but wrong once tax is applied.
Environments and datasets let one collection run anywhere
The same collection should run against local, staging, and production without editing every request. Environments hold the values that change between them, base URLs, keys, and account ids, as named variables the requests reference. Datasets go a step further, feeding a request a table of inputs so one flow exercises many cases: valid and invalid payloads, several account types, a range of amounts. You switch the environment or the dataset, not the tests.
Verify the contract, not just the status code
A 200 response is not a passing test. What matters is the contract: the shape of the body, the fields that must be present, the types they must have, and the values a dependent client relies on. Assert against a schema and you catch the changes a status code hides.
- A field renamed or dropped, which would break the interface that reads it.
- A number returned as a string, or a total that no longer matches the line items.
- An error response that stopped following its agreed shape, so clients can no longer parse it.
- A new required field on a request, caught before it reaches a real integration.
Mocks and webhooks for the parts you do not control
Not every dependency is available to hit on demand. Mocks let you stand in for a service that is slow, costly, or still being built, so a flow can be tested end to end before every piece is ready. Webhooks handle the other direction: when your system is supposed to call out on an event, you capture and assert that call, confirming the message was sent with the right payload rather than assuming it was.
Put together, API testing stops being a separate discipline and becomes part of the same quality story as your interface. The screen tells you the experience is right; the API checks tell you the foundation under it is right too. Run them together, against the environment you care about, and a green result means the whole journey holds, not just the pixels on top.
See these practices inside AxonQA
Generate structured test cases from your stories, then validate them with real runs on your own app.