How do you test API contracts with AI?
You describe the observable contract in plain English – "the create returns 201 with a string id; fetching that id returns 200 with quantity equal to 2" – and an AI agent proves it against your running API. It makes the requests, writes its own validation code, and passes or fails on the evidence, with no schema files or pact brokers to maintain.
Sub-use-cases
Covers Response-shape validation, required fields and types, negative contracts (the 401 is the pass), create-and-retrieve consistency and error-body checks.
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What an API contract test should cover
The contract a consumer can observe: the status code for each outcome, the fields that must exist and their types, the values named in the expected result, and the rejections – wrong auth, invalid input, missing fields – with the right status and a usable error body.
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How does AI verify a contract?
The agent calls the endpoint, saves the real response, and writes code against it: is this an array, do items carry an id, is quantity 2 on the order it just created. Multi-step contracts chain – it reads the generated id from the create and verifies the retrieve returns the same record. Every command and its output sit in the trace.
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When to run contract tests
On every deploy of the API, before consumers integrate against a new version, and whenever a contract-adjacent change ships – serializers, validation rules, auth middleware.
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Who needs API contract testing
Teams shipping APIs that other teams or paying customers integrate against, backends where a renamed field breaks someone else's production, and teams who tried consumer-driven contract tools and couldn't sustain the pact maintenance.
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How QA.tech helps
Consumer-driven contract tools like Pact verify agreements between specific consumers and providers at build time – that's a real strength, and if your consumers already write pacts, keep them. QA.tech verifies the contract from the other side: what the running API observably does, stated in plain language, with no pact files, broker, or consumer participation required. Teams use it where consumer-driven tooling was never adopted or quietly rotted – which is most teams.
FAQ
Common questions
- Is this the same as Pact or consumer-driven contract testing?
- No, and the difference matters. Pact verifies that a specific consumer's expectations match the provider, at build time, through pact files both sides maintain. QA.tech verifies the running API's observable behavior at test time, from plain-language expectations, with no pact files. They can coexist; most teams have neither and need the second.
- Do I need an OpenAPI spec or schema files?
- No. The contract is stated in the test's expected result. If you have an OpenAPI spec, it's a useful source for writing those expectations, but nothing needs to be generated or kept in sync.
- Can it verify error contracts?
- Yes. "Calling GET /account with an expired token returns 401 with an error message" is a complete test – the agent runs it and the 401 is the passing evidence.
- What happens when the contract legitimately changes?
- You update the plain-language expected result – one sentence – instead of regenerating schemas or republishing pacts. The next run verifies the new contract.
- Does it handle GraphQL?
- GraphQL is HTTP: the agent can post queries and validate the response shape. The plain-language authoring is designed around REST-style contracts, so start with a pilot test before committing a GraphQL suite.
Related use cases
Analytics & Tracking Event Testing
An AI agent completes the real user flow in a browser – a checkout, a signup, a form submit – then verifies the tracking request actually fired: URL pattern, method, status code, and the payload it carried. The purchase beacon with the wrong revenue value fails the test, before it fails the month-end numbers.
ReadAPI Regression Testing
You group API contract checks into a plan written as plain-language goals – "GET /orders returns 200 with a list, and every item has an id and a status" – and an AI agent runs them against your API environment on every deploy. The agent makes the requests, writes and executes its own validation code, and returns a verdict with the full request and command trace.
Read
Analytics & Tracking Event Testing
Next →API Regression Testing
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