The API contract broken
by one side, caught by the other
A backend team changes a field's type, or a frontend team starts relying on a field that was never actually guaranteed, and both sides find out only when something breaks in production, because nothing was checking that the contract between services was actually being honored. We build an agent that tests schema and API contracts continuously and flags a breaking change before either side ships it.
The process today
In any system with more than one service, an API, a shared database schema, or an internal library used by multiple teams, there is an implicit contract: this field will always be a string, this endpoint will always return this shape, this column will never be null. That contract usually exists only in people’s heads and in whatever documentation was written once and has not been updated since. A backend team renames a field, confident nothing depends on the old name, and a frontend or a partner integration that was quietly relying on it breaks in production with no warning.
The second cost is that this kind of break is specifically hard to catch with normal testing, because each team’s own test suite passes fine: the backend’s tests do not know about the frontend’s assumptions, and the frontend’s tests run against a mock that still has the old field. The contract violation only becomes visible when both sides run together in a live or integration environment, which is often production itself.
The third is contract drift that happens gradually and without any single breaking change: real usage slowly diverges from what the contract was ever meant to guarantee, a field that was documented as optional becomes something every consumer actually assumes is always present, until the day it genuinely is missing and several services break at once over something that was, technically, always allowed by the contract.
What the agent does
The agent builds a picture of the real contract for each API and shared schema by analyzing actual usage, which fields consumers genuinely read and depend on, not just what the provider’s documentation claims is guaranteed. Every proposed change, a field rename, a type change, a new required parameter, a database column modification, gets checked against that real contract, and anything that would break an actual consumer is flagged with exactly which consumer it affects, so the right team is looped in before the change ships rather than after something fails.
For database schema changes specifically, the check extends to every service that reads the affected tables, not just the one making the change, catching the common case in a shared-database architecture where one team’s migration breaks another team’s query. Where a breaking change is genuinely necessary, the agent recommends a versioning approach, a new API version, a transition period with both old and new fields present, rather than a single flag day that forces every consumer to update at once. A separate drift report surfaces places where real usage has quietly diverged from the documented contract, useful for catching fragility before it becomes an actual incident. Typical integrations: your CI pipeline, API gateway or schema registry, and the shared database itself for cross-service checks.
What stays with humans
Deciding how to resolve a genuinely necessary breaking change, whether through versioning, a coordinated migration, or direct communication with an external partner who depends on the API, is a decision made by the teams involved, with the agent’s analysis of who is actually affected as the starting point. Approving an exception for a flagged break that the provider and every affected consumer have already agreed to is a deliberate, logged decision, not something the check overrides on its own.
Guards
Every contract check and every flagged breaking change is logged with the specific consumer affected, building a record that makes cross-team coordination faster instead of a round of “who is actually using this field” questions after something breaks. The check runs as a required CI step, so a breaking change cannot merge silently without either a fix, a version bump, or an explicit, logged approval. A kill switch allows a known, coordinated breaking change to proceed without blocking on expected flags, while keeping the check active for anything unplanned.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Single automation | from $700 | Core APIs or shared schemas, contract analysis, breaking-change detection | 4 to 8 days |
| Department package | from $2,100 | Schema and contract tests plus database migration safety checks and CI/CD pipeline checks | 2 to 4 weeks |
Running cost is usually $15 to $40 a month in model usage depending on API and schema change frequency.
Related
This pairs well with database migrations with safety checks for the data layer specifically, and with CI/CD pipelines with AI code checks for the pipeline this plugs into as a required check. For the live verification layer on top of a working contract, see post-release smoke tests. Full package details are on the AI agents service page and the automation-everything overview; for systems with multiple services and a shared data layer, see the factory ERP recovery case study and the two-brand analytics hub case study.
Had one team’s change quietly break another team’s service before? Get in touch and we will map your real contracts.
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FAQ
How is this different from the database migration safety checks automation?
Migration safety checks focus on the database layer directly, locking and data-loss risk during a schema change. This focuses on the contract between services and consumers, APIs and schemas that multiple teams or systems depend on, catching compatibility breaks regardless of whether a database migration is even involved.
How much does contract testing automation cost?
From $700 for your core APIs or shared schemas, live in 4 to 8 days. A larger microservice architecture with many consumers usually runs $1,400 to $2,200.
How does it know what consumers actually expect?
By analyzing real usage, actual API calls and the fields they read, rather than relying solely on documentation, which is often out of date or was never fully accurate about what consumers really depend on.
Can it block a breaking change from merging?
Yes, as a required check in your CI pipeline, the same mechanism a failing test uses, so a breaking change either gets fixed, versioned properly, or explicitly approved with the affected consumer's team in the loop before it merges.
Does this work across microservices owned by different teams?
Yes, this is exactly the case it is built for: when team A's change can break team B's service and neither team has full visibility into the other's code, contract tests are the mechanism that catches it before production does.