OpenAI Decisions API: Turning AI Classification Into Predictable Workflows

AI applications are getting better at generating text, code, and explanations. The harder production problem is often narrower: should this item be routed to billing or technical support, does this image show damage, and how severe is this issue on a defined scale? OpenAI’s Decisions API, now generally available, is designed for those bounded questions rather than open-ended generation.

The distinction matters. A general-purpose response can be impressive but difficult to validate. A decision endpoint is useful when the application needs a typed answer, a probability distribution, or a score that can drive the next step in a workflow. OpenAI describes the API as a way to evaluate text, images, and JSON, with support for predicate, choice, and score questions. The official Decisions guide and create-decision reference document the current request and response shape.

The real change is a typed decision layer

Many teams have already built a prompt that says “classify this request.” The weak point is not always the model; it is the contract around the model. If the prompt can return an arbitrary paragraph, downstream code has to parse prose, infer confidence, and guess what to do with an edge case.

The Decisions API makes the question explicit. A predicate asks whether a condition is true and returns a probability. A choice selects from values you define, such as billing, technical, or other. A score evaluates an input against ordered levels, such as cosmetic, workaround available, and fully blocked. The result is still probabilistic, but it has a shape an application can inspect before taking action.

That turns AI from an isolated “call the model” feature into a small policy boundary. The application defines the allowed outcomes, the descriptions that separate them, and the threshold at which a human review queue should take over.

What the API can answer

The most obvious use case is routing. A support message can be sent with a choice question, and the application can direct it to a queue while keeping an other option for requests outside the taxonomy. A moderation or trust workflow can ask predicate questions about visible attributes. An operations team can score an issue against a rubric and prioritize the highest-risk cases.

Images are important because the question can attach its own evidence. A shared input can contain the reference material, while separate questions compare different candidate images against it. That is useful for product inspection, catalog cleanup, document review, and visual quality checks. JSON input is also supported, so an application does not need to flatten every structured record into a long string before asking a question.

The current guide also highlights practical scale improvements: shared input can be cached, requests can contain up to 400 questions, and publicly accessible image URLs can be supplied alongside inline data URLs. Those details do not remove the need for evaluation, but they make batch-style decision services easier to design.

Where it fits in a production stack

The API is not a replacement for every model call. OpenAI’s own guidance separates three jobs. Use Decisions when the answer is a predicate, a fixed choice, or an ordered score. Use Structured Outputs with the Responses API when the model must produce a richer object that follows your own JSON schema. Use function calling when the model should request a tool with arguments.

That separation gives builders a useful architecture:

1. Collect the evidence and normalize it into a stable input format.
2. Ask one or more narrow questions with clear, non-overlapping definitions.
3. Apply calibrated thresholds in application code.
4. Route uncertain cases to a human or a broader reasoning path.
5. Store the question version, answer probabilities, and final action for later evaluation.

The fourth step is the safety valve. A confidence value is not a guarantee, and a high probability can still be wrong when the taxonomy is incomplete or the evidence is ambiguous. The API should help a team make decisions more consistently, not hide uncertainty behind a confident-looking sentence.

A practical workflow for builders

Start with a small labeled set from the real workflow. Write down the cost of a false positive and a false negative. Then define questions around observable criteria rather than vague intent. If two categories overlap, improve their descriptions before adding more model complexity.

For a customer-support router, begin with four or five queues and an explicit fallback. For a visual inspection flow, use a predicate for each independent condition instead of one giant question that mixes damage, category, and severity. For prioritization, define two to ten ordered levels and make the difference between adjacent levels concrete.

Finally, monitor drift. Product names, support policies, and image distributions change. Keep a review sample, compare model answers with human labels, and revise thresholds when the cost of mistakes changes. Developers who need a broader tool-discovery workflow can also browse BTTC’s software catalog or return to the BTTC technology blog for related AI and developer-tool coverage.

Limits and safeguards

A typed response is not the same as a verified fact. Do not use a probability as an authorization to take irreversible action without a policy check. Keep credentials server-side, validate every returned value, log the model and question version, and protect sensitive images and customer data according to the application’s privacy requirements.

Also avoid turning a rubric into a hidden business policy. The descriptions sent to the model should be reviewable by the people who own the workflow. If the business cannot explain why an input belongs in a category, the model will not make the policy clearer by itself.

Frequently asked questions

Is the Decisions API the same as Structured Outputs?

No. Decisions is optimized for predicates, fixed choices, and ordered scores. Structured Outputs is better when the application needs a richer object that follows a custom schema.

Can it analyze images?

Yes. Images can be part of shared input or attached to individual question instructions. The API supports public HTTP or HTTPS image URLs and inline base64 data URLs, subject to the platform’s input limits.

Should every answer be trusted automatically?

No. Use labeled examples to set thresholds, keep a fallback or human-review path, and treat probabilities and confidence as signals for policy—not as proof.

How does this help a small software team?

It reduces the amount of glue code needed to turn a model judgment into a predictable branch. A team can begin with one narrow routing or inspection workflow, measure it, and expand only when the data supports it.

Conclusion

The Decisions API is interesting because it narrows the gap between multimodal AI and ordinary application logic. Instead of asking a model to write an answer and then reverse-engineering that answer into a branch, developers can define a small set of typed questions and make uncertainty visible. That is a practical pattern for support routing, visual inspection, triage, and other workflows where the next action matters more than a polished paragraph.