An AI endpoint still accepts an authenticated request and returns a response, but that response may be structured, streamed in pieces, or part of a longer tool-use or background-processing cycle. Applications therefore need to handle more than a single request followed by one final text payload.
What stays the same—and what changes
The basic exchange remains familiar: a client sends structured input to an API endpoint, and the service returns a result. OpenAI describes its interfaces as RESTful, streaming, and realtime; those are OpenAI-specific examples, not a guarantee that every provider uses the same endpoint names or event formats. See the OpenAI API reference.
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In a conventional API flow, an application often sends a request and handles one completed response. An AI endpoint can still work that way, but its input and output may contain multiple kinds of content, and generation can involve intermediate events or application-managed follow-up calls.
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AI requests can combine modalities rather than supplying only a text field. OpenAI’s quickstart demonstrates text and image inputs, while its API reference describes responses with different item and content types. Applications should parse the documented response structure instead of assuming the first returned item is user-facing text. See the OpenAI quickstart.
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Authentication remains part of the flow. OpenAI documents bearer-key authentication for HTTP requests, and its quickstart demonstrates a server-side SDK call. Keep API secrets on a trusted server or in a managed secret store, never in client-side code where users can inspect them.
What happens when an AI endpoint makes a tool call
A model can be given tools, such as custom functions that connect to an API, a data source, or code. If it requests a function, the model has not executed that operation: the application must interpret the request, run the function, and send the result back in a follow-up interaction. OpenAI documents this tool-use pattern in its API reference.
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- Send the model input along with the tools the application allows it to request.
- Inspect the response for a requested tool call and validate its arguments against the expected schema.
- Check authorization in application code, then execute only the permitted operation.
- Return the tool result to the model in the required follow-up interaction and process the resulting response.
This boundary matters: a model-generated request is not proof that a user is authorized to perform the action. Enforce permissions and validate inputs in the application, particularly before operations that modify data or trigger external effects.
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With streaming enabled, the server can send events while a response is being generated instead of waiting to deliver one complete result. OpenAI’s streaming reference says that setting stream to true causes the server to emit server-sent events as the response is generated: Streaming responses.
A streaming client can render partial output sooner, but it must treat the connection as an event stream—not as a single JSON response. Handle events in order, distinguish intermediate output from completion, and account for interruption and errors. The specific event names and payloads depend on the API being used.
When work becomes asynchronous
Some AI work is better handled outside a request that waits for a complete result. OpenAI documents Batch processing as asynchronous, with explicit lifecycle statuses. Its Responses API also supports background work that can be polled. Applications using these patterns need to represent progress and terminal states rather than treating the initial request as the finished result. See the Batch guide and Background mode guide.
- Track the status of each submitted job and show meaningful progress to the user.
- Define how polling, retries, and cancellation work for the specific endpoint.
- Handle failures and expiry so a job does not appear pending indefinitely.
Operational controls to account for
AI applications need familiar API operations practices, plus attention to model and endpoint behavior. OpenAI’s API reference documents request IDs and rate-limit headers that can help with troubleshooting and traffic management. It also notes that model behavior can vary between snapshots, and recommends pinned model versions and evaluations when consistency matters. Consult the current API reference for the relevant headers and behavior.
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Check data handling for the exact endpoint
Retention depends on the endpoint, feature, and configuration. OpenAI’s current data-controls documentation, accessed in 2026, states that the Responses API has a 30-day application-state retention period by default when that state is stored. It also says background mode uses temporary storage for polling, while remote MCP services have their own retention policies. These are OpenAI-specific details, not general rules for AI APIs. Review the data controls documentation for the endpoint, account settings, and data types you plan to use.
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