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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteTo build an LLM agent, first define the task and the runtime that will manage it; then create evaluations, decide whether supervised fine-tuning is justified, connect tools and state, and deploy with monitoring and reliability checks. Fine-tuning can improve how a model handles a task, but it does not replace the agent’s orchestration, tool execution, or evaluation. The steps below use OpenAI documentation as a concrete example; model availability and implementation choices vary by provider and workload.
Plan the agent before choosing a model
Start by describing the job the agent must complete, the inputs it receives, and what a successful result looks like. Identify which actions require tools, what information must persist between steps, and where execution should happen. These decisions shape the runtime and evaluation; choosing a model first can lock you into a setup that does not fit the task.
Write down the task and its boundaries
- State the intended outcome in terms that can be judged—for example, whether a support request was routed correctly, rather than whether the response merely sounded helpful.
- List the information the model may use, the actions it may take, and any actions that need human approval.
- Define what the application should do when the model is uncertain, a tool fails, or required information is missing.
- Decide what data and state need to be retained, and what execution environment is acceptable for the workload.
These are design questions, not fine-tuning settings. Answering them gives you criteria for comparing runtimes and building evaluations.
Choose who owns the agent loop
In OpenAI’s documented options, the main distinction is how much of the agent runtime your application owns. The Agents API is a managed harness, the Agents SDK runs in your application, and direct Responses API integration offers a more direct path. The right choice depends on the control and operational responsibility your team wants. See OpenAI’s Agents overview and Agents SDK guide.
| Option | Where the runtime is managed | Consider it when |
|---|---|---|
| Agents API | OpenAI provides a managed harness. | You want a managed agent path and are comfortable with the control and integration trade-offs it entails. |
| Agents SDK | Your application runs the SDK runtime. | You want an application-side runtime for orchestration and tools, and your team is prepared to own its integration and operations. |
| Responses API | Your application integrates more directly with the API. | You want a direct integration path and are prepared to implement the surrounding agent workflow that your application needs. |
Compare options against the same practical questions: who implements and executes tools, who manages state, where execution and storage occur, how approvals fit, and how the runtime integrates with the rest of your application. A managed harness reduces some runtime ownership; an application-side or direct approach gives your team more responsibility for designing and operating the workflow. The OpenAI documentation describes these as options, not as a universal ranking.
Build evaluations before fine-tuning
Create a representative evaluation set before spending effort on model optimization. Include realistic inputs, expected outcomes or scoring criteria, and cases that expose likely failures. Keep examples that reflect ordinary use as well as meaningful edge cases. The point is to establish a baseline and a repeatable way to tell whether a change improves the intended task.
OpenAI’s supervised fine-tuning guide makes the priority plain: “Good evals first!” It recommends setting up reliable evaluations before investing in fine-tuning, then comparing the resulting model against the task. Read the OpenAI supervised fine-tuning guide for its dataset preparation and evaluation workflow.
Decide whether supervised fine-tuning is warranted
Fine-tuning changes model behavior using examples; it does not define the task, provide tools, or operate the agent loop. Consider it only after a baseline evaluation shows a recurring model-behavior problem that examples can address. If the problem is instead missing information, an unreliable tool, unclear instructions, or a poorly defined success criterion, address that part of the system first.
Use example counts as starting guidance
OpenAI’s current supervised fine-tuning guide says the minimum is 10 examples. It reports having seen improvements with 50–100 examples and recommends starting with 50 well-crafted demonstrations, while noting that the appropriate number varies by use case. These are OpenAI’s guidance, not a guarantee that a particular count will improve a given application. Judge the result with your evaluation set rather than treating the count as a success threshold.
Prepare, run, and evaluate the fine-tuning job
The documented OpenAI workflow is to prepare the dataset, upload it, create a fine-tuning job, and evaluate the resulting model. Check the guide for current model eligibility and limits before selecting a base model: availability and constraints can change. Keep the evaluation criteria consistent when comparing the fine-tuned result with the baseline, so any improvement or regression is visible.
Fine-tuning is a model-optimization step inside a larger system. A fine-tuned model still needs the chosen runtime, application tools, state handling, and deployment checks.
Integrate tools, state, and approvals
Once the runtime is chosen, connect the capabilities the task actually requires. In an application-side setup, the SDK can provide orchestration and tools; the specific execution, state, and approval design still depends on the application. The OpenAI Agents SDK agent documentation describes its agent building blocks.
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- Tools: Specify which operations the agent may invoke and how your application executes them. Test both successful calls and failures.
- State: Decide what context must carry across interactions and where it belongs in your architecture. Avoid assuming the model itself is the appropriate place to store application state.
- Approvals: Identify actions that should pause for human review, and make that approval path part of the workflow rather than an informal expectation.
- Execution environment: Choose where tool code runs and what access it needs. The runtime choice should fit your requirements for control, integration, and operational ownership.
Keep the model’s role distinct from the application’s responsibilities: the model can help select or use capabilities, while the application and runtime determine how those capabilities are executed and governed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Deploy with evaluation and operational checks
Deployment is not just making an endpoint available. Before release, test the model and the complete tool workflow against the task criteria. OpenAI’s API deployment checklist covers model choice, evaluation, tool calling, observability, reliability, latency, and cost. Exact thresholds and implementation choices depend on the workload.
Pre-release checks
- Model choice: Confirm the selected model is available for the intended use, including fine-tuning eligibility if applicable.
- Evaluation: Run the representative set against the deployed configuration, not only against a model in isolation.
- Tool calling: Verify tool inputs and outputs, failure behavior, and any approval requirements.
- Observability: Ensure the team can inspect enough of the system’s behavior to identify model, tool, and integration problems.
- Reliability: Exercise expected failure cases and check that the application responds appropriately.
- Latency and cost: Measure these in the context of the actual workflow, then decide whether they meet the application’s needs.
Keep evaluation and monitoring aligned: the same task definition that justified the design should help you detect when the deployed system stops meeting it. Recheck model availability and relevant limits when the system changes or is rebuilt, since those details are subject to change.
Quick Recap
A practical sequence from prototype to production
- Define the job: Specify inputs, expected outcomes, boundaries, required actions, and failure handling.
- Select the runtime: Choose a managed harness, an application-side SDK, or a direct API integration based on control and operational ownership.
- Establish a baseline: Build representative evaluations and test the initial model and workflow.
- Diagnose gaps: Determine whether failures come from model behavior, missing context, tools, orchestration, or task definition.
- Fine-tune only if it addresses the diagnosed gap: Follow the provider’s current preparation and job workflow, then compare results against the baseline evaluation.
- Integrate and govern: Implement tools, state, execution boundaries, and human approvals where required.
- Deploy and monitor: Check evaluation results, tool behavior, observability, reliability, latency, and cost in the intended application context.
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