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An AI agent can call an API by requesting a tool that your application has made available. The model chooses the tool and supplies structured arguments; your application or a configured service validates the request, runs the operation, and returns the result. The model does not automatically gain direct or unrestricted API access.
What does it mean for an AI agent to call an API?
In this workflow, “call” means the model returns a structured request for an operation—often called a tool call or function call. Your application defines what operations are available and handles their execution. For example, if you provide a get_weather function, a model might choose it for the question “What is the weather in Paris?” and supply Paris as an argument. The handler, not the model itself, makes the weather request and returns the result.
OpenAI describes tool calling as “a multi-step conversation between your application and a model via the OpenAI API.” Anthropic describes tool use as a way for Claude to call functions a developer defines or tools Anthropic provides. The broad pattern exists across providers, but their schemas, execution behavior, and supported features are not necessarily interchangeable.
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- Send a request with available tools. Your application tells the model which tools it may request, including each tool’s name, purpose, and argument shape.
- Receive a tool call. The model can return a structured request naming a tool and providing arguments. This is a request to your system, not proof that the operation has run.
- Execute the handler. Your application checks the request and runs the code or service associated with that tool.
- Return the result. Send the handler’s output back into the conversation or session so the model can use it.
- Continue or finish. The model can respond to the user or request another tool. With OpenAI’s Responses API, the workflow can continue for as many calls as the task requires.
A function tool commonly has a name, a description of when it should be used, and a JSON Schema describing its arguments. Schema-constrained configurations can help enforce the expected shape, but schema support depends on the model and request configuration; unsupported or nonconforming schemas may be rejected. Schema validation also does not replace checking whether a requested action is authorized.
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Who runs the API operation?
The execution location depends on the tool and integration. With a client-side tool, your application runs the handler—for example, it might call a weather service after receiving the model’s request. Some providers also offer server-side tools that the provider executes. In either case, distinguish the model’s choice of an operation from the system that actually performs it.
The application or configured runtime is the enforcement boundary for your own permissions and business rules. A model-generated request should not, on its own, grant access to an account, approve a transaction, or override an authorization policy.
Which implementation route should you choose?
Tool calling is a workflow, not a single interchangeable product choice. OpenAI’s documentation describes several routes, including managing the loop through the Responses API, using the Agents SDK, using the managed Agents API, and connecting tools through mechanisms such as remote MCP. Built-in tools and tool search can also extend what is available. Compare the routes by who owns orchestration and state, where tool code executes, integration effort, and compatibility with the model and runtime you intend to use.
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- Responses API: Manage the request-and-result loop in your application when you want to control how it proceeds.
- Agents SDK: Use an SDK-oriented approach when reusable agents and handoffs are useful to your workflow.
- Managed Agents API: Consider a managed route when its orchestration model and supported capabilities fit your needs.
- Remote MCP and other tool connections: Use a connection mechanism when you need to expose tools through that protocol or service.
- Built-in tools and tool search: Consider these when the selected model and runtime support them and they match the task.
Check current provider documentation for model compatibility and configuration details before committing to a route; features and availability can change.
How do you make tool calls safer and more reliable?
- Keep each tool narrow. Give it a specific job and a clear description so the model has less ambiguity about when to request it.
- Make inputs explicit. Define argument types and required fields in the schema, then validate the received values in the handler.
- Enforce authorization in application code. Check the user, resource, and requested action against your own access rules. Do not treat a well-formed model request as authorization.
- Return useful results. Send back a clear success value or actionable error so the model can respond appropriately or decide what to do next.
- Require review for consequential actions. For operations such as approvals, use human review or other guardrails appropriate to the impact. OpenAI’s agent guidance highlights guardrails and human review for tools that affect approvals.
What should you remember?
An AI agent can request API-backed operations through tools, but the surrounding application or configured service defines and executes those operations. The handler is where you validate inputs, enforce permissions, and decide whether an action needs human approval. Choose an integration route that fits your needs for orchestration, state, execution location, and supported features.
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