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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →To get an AI agent to use an API reliably, turn the documentation for the task into a short, explicit procedure—and make sure every step matches a tool the agent can actually use. Keep the full API reference as the authoritative source; the procedure is a task-specific guide, not a replacement for the docs. This is a system-design and writing practice, not a claim that agents are incapable of reading documentation.
Why give an agent a procedure?
An API reference is written to describe a system broadly. An agent working on a particular job needs to know which operations matter, what inputs they require, what order to follow, and what to do when a needed detail is missing. A procedure makes those choices explicit.
Giving an agent the full reference may still be useful, especially when it must discover operations dynamically. But documentation alone does not necessarily tell it which operation to select for a particular task or how your application expects it to handle failures and uncertainty. A procedure narrows the job. There is no controlled head-to-head evidence here showing that procedures always outperform raw documentation, so treat this as a design method to evaluate on your own tasks—not a universal performance guarantee.
How to turn API documentation into agent instructions
- Start with a concrete task. Write down the outcome the agent must produce, then identify the API operations needed to reach it. Consult the full reference as the source of truth; extract only the relevant behavior, required inputs, constraints, and failure cases.
- Define the job and its boundaries. State what the agent should accomplish, what it must not assume, and when it should stop or ask for clarification. OpenAI describes instructions, tools, and controls as parts of configuring agent behavior; they should be designed to work together (OpenAI’s agent configuration guide).
- Write ordered directions. Use numbered steps with one clear action or decision per step. OpenAI’s practical guide gives a useful transformation prompt: convert relevant help-center material into a numbered list of directions for the agent, written without ambiguity (A practical guide to building agents). That prompt is a starting point, not proof that the resulting procedure is correct.
- Match each step to an available tool. Name the operation the agent can invoke and specify the arguments or conditions it needs. Do not instruct it to perform an action that its runtime does not expose. OpenAI distinguishes a managed Agents API, an application-controlled Agents SDK, and direct Responses API use; the right configuration depends on which route your application uses (OpenAI’s agents overview).
- Check representative tasks and outputs. Try the procedure on realistic examples, including missing inputs and likely errors. Inspect whether the agent chose the intended operation, supplied valid arguments, followed the sequence, and stopped appropriately. Update the directions when actual behavior reveals a gap; do not treat clear wording alone as validation.
- Keep the procedure current and within runtime limits. When the API changes, review the affected steps against its reference. For the OpenAI Agents API specifically, combined instructions and tool configuration should stay below 4 MiB (4,194,304 bytes), leaving room for API metadata, according to its configuration documentation. That is a product-specific constraint, not a general limit for all agent platforms.
Keep the agent focused; add complexity only when needed
Begin with one agent that owns a clearly bounded task, then add tools incrementally as the task requires them. OpenAI’s agent-definition guidance recommends starting with the smallest agent that can own the work; multiple agents are more justified when distinct responsibilities require separate instructions, tool surfaces, or approval policies (OpenAI’s agent definitions). Splitting work without a meaningful separation adds coordination and maintenance burden rather than automatically improving API use.
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Choose the runtime before writing configuration-specific instructions
The procedure-writing method is portable, but tool wiring and control differ by runtime. OpenAI’s overview describes these options:
| OpenAI option | Who controls the work | When the overview says it fits |
|---|---|---|
| Agents API | OpenAI manages the agent and saves progress. | Long-running work. |
| Agents SDK | Your application controls deployment, storage, approvals, and runtime integration. | When you need application-level control of those elements. |
| Responses API | Your application makes direct model calls or builds an agent from scratch. | Direct calls or a custom-built agent. |
These distinctions affect who owns state and tool execution. Write instructions for the environment in which the agent will actually run, rather than assuming that a procedure configured for one route transfers unchanged to another. The Agents API quickstart, for example, demonstrates creating a session with an agent configuration, sending a task, streaming events, and collecting a final result; it also advises keeping the API key outside the agent sandbox. That is an OpenAI quickstart example, not a universal API architecture.
Rank #2
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What a benchmark does—and does not—show
A June 24, 2025 arXiv paper by Xinyi Ni, Haonan Jian, Qiuyang Wang, Vedanshi Chetan Shah, and Pengyu Hong reports that Doc2Agent achieved a “55% relative performance improvement with 90% lower cost compared to direct API calling on WebArena benchmark.” The authors describe a method that generates executable tools from API documentation and iteratively refines them with a code agent (Doc2Agent paper). Those figures apply to that paper’s method and benchmark; they do not establish that an ordinary written procedure will deliver the same improvement or that the result generalizes to every API.
Quick Recap
Best Value
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Rank #4
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Rank #3
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