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What the Decisions API does—and where its boundary is
OpenAI describes the Decisions API as an endpoint to “Evaluate ordered classification and scoring questions against shared input. Answers are returned in question order.” It supports predicate, choice, and score questions, so an application can ask for outcomes such as whether a condition is met, which supplied option applies, or where evidence falls on an ordered scale. See the OpenAI Decisions API reference.
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The endpoint is a decision interface, not a complete workflow engine. Its documented input is a string or user messages containing text and inline images. The reference excludes external image URLs, files, audio, tool calls, and item references. If the decision depends on those inputs or must trigger and manage actions, you will need a surrounding application or a different orchestration layer.
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| Option | Best fit | Control and orchestration |
|---|---|---|
| OpenAI Responses API | Direct model responses and integrations where your application controls the experience. | Developer-controlled integration; a starting point for building an agent from scratch. |
| OpenAI Agents SDK | Agent applications where the application should own the agent loop, tools, deployment, storage, and approvals. | Application-owned agent runtime and orchestration. |
| OpenAI Agents API | Long-running agent tasks where managed execution and saved progress are useful. | OpenAI-managed runtime with progress persistence. |
| Amazon Bedrock Prompt Flows | Visual composition of prompts, supported foundation models, and AWS services such as Lambda. | AWS workflow-building option; not a drop-in endpoint with the Decisions API contract. |
| Temporal with the Agents SDK | Durable, long-running workflows that may wait, resume, or require human input. | Workflow orchestration focused on persistence and resumable execution. |
OpenAI’s Agents runtime comparison explains the differences among its own runtime choices. AWS describes Prompt Flows in its generative AI service decision guide as visual workflows that connect prompts, supported models, and AWS services, with deployment from testing to production. The Agents SDK documentation also identifies Temporal for running agents where durable, long-running execution—including human-in-the-loop tasks—is important.
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Choose by decision contract, inputs, and control
Match the answer shape
Use the Decisions API when your application can express the task as ordered predicate, choice, or score questions evaluated against common evidence, and when receiving answers in question order suits the consuming code. If the system needs free-form actions, branching procedures, or a managed multi-step loop, select a runtime or orchestrator that owns those behaviors rather than treating a classification endpoint as a workflow substitute.
Check the evidence you need to pass
Text and inline images fit the documented Decisions input. If the system must use files, externally hosted images, audio, tool results, or other evidence that the reference excludes, plan an integration that prepares supported input or choose an architecture whose components can handle the required sources.
Decide who owns state and execution
With the Responses API or Agents SDK, the developer retains direct control of the integration or agent loop. The Agents API is intended for a managed long-running runtime with saved progress. Prompt Flows suit visual composition within AWS, while Temporal addresses durable execution and resumption. The important distinction is where state lives and who is responsible for continuing work after waits, failures, or approval pauses—not merely which model produces a response.
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Build safety checks at the action boundary
For systems that can call tools or make consequential changes, put validation and approval where those effects can occur. OpenAI’s guardrails and human review guide maps different controls to different boundaries:
- Input guardrails: block disallowed requests before work proceeds.
- Output guardrails: validate or redact the final response.
- Tool guardrails: check arguments or results for tools that need them.
- Human approval: pause before a tool performs a side effect that should not happen automatically.
These controls do not automatically apply to every stage of a chain: input guardrails run only for the first agent, output guardrails only for the final-output agent, and tool guardrails only for tools to which they are attached. If every side-effecting call needs a check, attach validation or approval to each relevant tool or enforce it in the service that executes the action.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate the workflow, not just the final answer
A decision system can return a plausible answer while choosing the wrong tool, handing off incorrectly, or failing to follow an instruction. OpenAI’s agent evaluation guide recommends using traces and graders while debugging behavior, then datasets and evaluation runs for repeatable comparisons. Trace grading can examine tool choice, handoffs, instruction-following, and changes after routing or prompt updates. Keep representative examples so a change that improves one path can be checked against regressions elsewhere.
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A practical selection sequence
- Define the output: if it is a predicate, supplied-value choice, or ordered score over shared text or inline-image evidence, assess the Decisions API first.
- List the required inputs: identify any files, URLs, audio, tool outputs, or other evidence the Decisions reference excludes.
- Locate the workflow boundary: choose whether the application, OpenAI-managed runtime, AWS flow, or durable workflow service should own state, retries, waits, and orchestration.
- Map side effects: identify every tool or service that can change data or trigger an external action, and decide where validation and human approval must happen.
- Test actual behavior: inspect traces during development and use repeatable evaluation data to compare changes before relying on the system.
Migration and evidence caveats
OpenAI’s March 11, 2025 agent tools announcement presented the Responses API and Agents SDK as building blocks for agentic applications and noted that Chat Completions remained appropriate for developers who did not need built-in tools. The announcement described a planned Assistants API deprecation targeted for mid-2026, conditional on a future formal announcement. That statement alone does not establish the current deprecation status; consult current migration documentation before planning a migration.
The official documentation establishes architectural options and their intended control models, not a market-wide ranking or feature parity. It does not supply a comparable benchmark for latency, throughput, or performance, and this comparison does not establish comparative pricing. Product features and migration guidance can change, so verify current provider documentation for the implementation you choose.
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