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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesTo get an AI agent to use an API reliably, turn the documentation relevant to its task into a short, ordered procedure—and connect every step to a tool the agent can actually use. Keep the full API reference as the developer’s source of truth. The procedure is not a replacement for the docs; it makes the necessary operations, inputs, constraints, and stop conditions explicit for one job.
Why write a procedure instead of handing over the whole reference?
API documentation is written to describe a product broadly. An agent working on a specific task needs a narrower answer: which operation to use, what information it needs, what order to follow, and what to do when a prerequisite is missing or the result is unexpected.
That does not mean every agent is incapable of reading documentation. It is a system-design choice: the developer selects the information needed for a task and operationalizes it as directions. OpenAI’s practical guide to building agents gives a concrete example prompt: convert help-center material into a numbered list of clear, unambiguous directions written for an agent. That prompt is a useful transformation pattern, not a guarantee that the resulting procedure will be correct.
There is no controlled head-to-head result in the cited sources showing that a written procedure always outperforms giving an agent raw API documentation. The practical case for a procedure is specificity: it makes the intended path and boundaries easier to inspect, test, and maintain.
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How to turn API documentation into agent instructions
- Start with a real task. Describe the outcome the agent must produce, then identify the API operations needed to reach it. Keep the complete reference available to the developer; extract only the relevant behavior and constraints for the agent’s procedure.
- Set the job and its boundaries. State what the agent should accomplish, what it must not assume, and which conditions require it to stop or ask for clarification. Instructions are only one part of agent configuration: they work alongside tools and controls.
- Write the steps in order. Use direct directions with the required inputs and expected outcomes. Replace vague language such as “update the record” with the actual action and the conditions for taking it. If the next step depends on a result, say what result to check.
- Match each direction to an available capability. Name the tool or API operation the agent can invoke. Do not tell it to inspect a dashboard, request approval, or call an endpoint unless the chosen runtime actually exposes that capability.
- Check the procedure against representative tasks. Review whether the directions cover the expected inputs, response variations, and failure cases. Inspect actual outputs before treating the procedure as reliable; merely converting documentation into numbered steps does not establish correctness.
- Maintain it with the API. When the reference or available tools change, check whether the procedure’s operation names, sequence, inputs, and boundaries still match. Keep the reference authoritative and the procedure focused on the task.
Choose the runtime before finalizing the instructions
The procedure can be portable in concept, but its tool calls and configuration must match the runtime. OpenAI describes three routes with different ownership boundaries:
| OpenAI route | Who controls what | When the overview says it fits |
|---|---|---|
| Agents API | OpenAI manages the agent for long-running work and saves progress. | When you want a managed agent experience for longer-running tasks. |
| Agents SDK | Your application controls deployment, storage, approvals, and runtime integration. | When you want the application to own those runtime decisions. |
| Responses API | Your application makes direct model calls or builds an agent from scratch. | When you need direct API use or want to assemble your own agent. |
These distinctions affect who owns state and tool execution, so a procedure written for one setup may not work unchanged in another. See OpenAI’s Agents overview and Agents API configuration guide for the relevant product-specific details.
For example, OpenAI’s Agents API quickstart shows a lifecycle in which an application creates a session with an agent configuration, sends a task, streams events, and collects a final result. It also advises keeping the API key outside the agent sandbox. That is guidance for this OpenAI example, not a universal requirement for every agent architecture.
Start with one focused agent; add complexity for a reason
Begin with the smallest agent that can own a clearly defined task. Add tools incrementally as that task requires them. OpenAI’s agent definitions guidance recommends adding agents when distinct ownership, instructions, tool surfaces, or approval policies justify splitting the work—not simply because a workflow has several steps.
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For the OpenAI Agents API specifically, the combined instructions and tool configuration should remain below 4 MiB (4,194,304 bytes), leaving room for API metadata. This is a product-specific configuration limit, not a general limit for all agents or APIs; check the current documentation for the runtime you use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What a published benchmark does—and does not—show
A paper by Xinyi Ni, Haonan Jian, Qiuyang Wang, Vedanshi Chetan Shah, and Pengyu Hong, dated June 24, 2025, reports a “55% relative performance improvement with 90% lower cost compared to direct API calling on WebArena benchmark” for Doc2Agent. The approach described in its abstract generates executable tools from API documentation and iteratively refines them with a code agent. Those figures belong to that research method and benchmark; they are not evidence that an ordinary written procedure will produce the same gains across APIs. Read the paper on arXiv.
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