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Put each provider behind an integration boundary
Let the rest of your application depend on an internal interface that describes what it needs, rather than on a provider’s endpoint paths, request format, response shape, or error codes. The integration layer should own those provider-specific details and translate them into the application’s internal types and error categories.
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This boundary gives you one place to update when a provider changes its contract, and makes it easier to support more than one provider without spreading provider-specific assumptions throughout the application.
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OpenAPI is a language-agnostic format for describing HTTP APIs. A description can support documentation, generated clients, and tests. Keep it aligned with the provider contract and the generated code and toolchain you actually use: a generated client reflects the description and tooling it was built from, not every change that may later occur.
Generated code also cannot decide which application behaviors matter. Validate required fields and business-level invariants at the boundary, and test user-relevant outcomes in your own application.
Define what counts as compatible
“Backward compatible” is not a universal rule. It depends on the API’s stated policy and on the assumptions your client makes. Microsoft’s API guidance identifies removals, renames, behavior changes, and error-contract changes as examples of breaking changes. It also notes that API teams may disagree about whether adding a JSON response field is compatible.
Write down your client’s parsing assumptions so that maintainers can assess a change instead of guessing.
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| Contract change or condition | Client concern | Design response |
|---|---|---|
| A required field is removed or renamed | Parsing or downstream application logic may fail. | Treat it as breaking for clients that rely on the field; coordinate a version or migration. |
| Behavior changes while the structure stays the same | The response may parse successfully but no longer meet application expectations. | Test the behavior your application relies on, not only whether the response matches a schema. |
| Error shape or meaning changes | Retry, fallback, or user-facing handling may take the wrong path. | Normalize provider errors at the boundary and explicitly test error cases that trigger special behavior. |
| An unfamiliar optional response field appears | A strict parser may reject an otherwise usable response; a permissive parser may ignore a meaningful change. | Follow the provider’s compatibility policy. Where that policy allows it, tolerate unknown optional fields while validating required fields and semantic invariants. |
| A new event or stream item appears | The client may not know whether to ignore, surface, or reject it. | Document recognized event types and define safe handling for unrecognized types rather than leaving behavior accidental. |
The table is a design aid, not a promise that every provider treats these changes alike. Use the provider’s published compatibility rules where available.
Version deliberately and plan the migration
Prefer an additive change when it fits the existing contract and preserves existing client behavior under the API’s compatibility policy. When structure or behavior must break, make the client’s requested version explicit and plan the move before the old version is retired.
Microsoft’s Azure API design guidance discusses URI, query, header, and media-type versioning approaches, which differ in how clients select a version, how servers route it, and how caches distinguish responses. Kubernetes API lifecycle guidance describes serving multiple versions while clients move from a deprecated version to its replacement. The right choice depends on the API and its consumers; assess these questions before committing to one:
- Can a client and an operator readily see which contract version is in use?
- Can the server route and support the required versions?
- Will caches distinguish responses for different versions correctly?
- How long must old clients continue to work?
- Do the documentation and migration examples explain the change clearly?
For a retirement, communicate the replacement and migration path, support the transition period your consumers need, and test both the old and new contract while both are served. Do not treat a deprecation notice as proof that all clients have moved.
Retry according to operation semantics
A timeout means the client did not receive a timely result; it does not prove that the server never applied the request. The IETF’s RFC 9110 states: “A client SHOULD NOT automatically retry a request with a non-idempotent method unless it has some means to know that the request semantics are actually idempotent, regardless of the method, or some means to detect that the original request was never applied.”
Decide retries per operation, not just by HTTP status code. Before retrying a POST-based AI request, determine whether repeating it is safe, whether it can trigger external side effects, and whether duplicate computation or duplicate results are acceptable. Use a provider-supported mechanism for recognizing duplicate requests only when its semantics are documented; do not assume one exists. If you cannot establish that retrying is safe or that the original request was not applied, surface the uncertain outcome rather than silently repeating it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate model changes as behavior changes
A stable API schema does not guarantee stable AI behavior. OpenAI documents that prompting behavior can change between model snapshots and recommends pinned model versions and evaluations to improve consistency. This is provider-specific guidance, not a guarantee about every provider’s model lifecycle.
Keep model selection separate from application logic and record the selected model alongside relevant configuration. Before moving to a new snapshot, run representative application cases against it and compare the behaviors your product depends on.
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Shape evaluations around the application rather than assuming a universal test set. Depending on the integration, useful checks may include structured fields, tool selection, refusal handling, and correct assembly of streamed output. An evaluation can reveal regressions in those cases, but it cannot guarantee that every production input will behave as expected.
Make failures diagnosable without over-logging
When the provider returns a request identifier, capture it with your own trace identifier, provider and model selection, endpoint, timing, and a normalized error category. OpenAI recommends logging request IDs for production troubleshooting and documents a client-supplied request ID for network failures where a server-generated ID may not reach the client.
Keep credentials and sensitive prompt or response content out of routine logs unless your data-handling requirements explicitly permit their collection. The goal is to retain enough context to locate and classify a failed request without turning diagnostic records into an unnecessary copy of user data.
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