Yes—an AI agent can use an API when its application makes that API available as a defined tool. The model chooses whether to request the tool and supplies structured arguments; application code or a configured service performs the operation, then returns the result for the model to use. That is controlled tool use, not unrestricted API access.
How does an AI agent call an API?
OpenAI describes tool calling as a multi-step exchange between an application and a model. The model does not independently connect to an arbitrary API: the developer exposes a tool, and the application handles execution.
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- Send the model a request and the available tools. Each tool tells the model what operation it can request.
- Receive a tool call. The model may select a tool and provide arguments—for example, choosing a developer-defined
get_weatherfunction and passingParis. - Execute the operation in the application. The application’s handler validates and runs the request against the relevant service.
- Return the tool result. The result is added to the ongoing conversation or session.
- Continue the exchange. The model can answer using the result or request another tool call if the task needs one.
With OpenAI’s Responses API, this sequence can continue for as many calls as the task requires. The application or configured runtime remains responsible for execution. OpenAI’s function-calling guide describes the exchange as “a multi-step conversation between your application and a model via the OpenAI API.”
What does a developer define?
A function tool typically has a name, a description of when to use it, and a JSON Schema describing its arguments. The developer also writes the handler that carries out the requested operation and returns a result. For instance, the model may request a weather lookup with a city argument, while the application decides how to query the weather service and what information to return.
Some configurations support strict schema constraints, but strict mode is not a guarantee that every schema will work: unsupported or nonconforming schemas can be rejected, and compatibility depends on the model and request configuration. See OpenAI’s function-calling documentation for the applicable requirements.
Who executes the tool call?
Execution depends on the tool and runtime. For a function implemented by the developer, the application runs the handler after receiving the model’s structured request. A managed or built-in tool may instead be executed by the service providing that capability. The distinction matters: the model proposes an operation, but the execution boundary determines what actually runs and under which permissions.
This pattern is not unique to OpenAI. Anthropic says, “Tool use (also called function calling) lets Claude call functions that you define or that Anthropic provides.” In that description, client tools are executed by the application, while server tools are executed by Anthropic. These examples establish a similar broad pattern, not interchangeable schemas or identical feature support across providers. See Anthropic’s tool-use overview.
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Which implementation route should you choose?
Tool calling is a capability, not a single architecture. OpenAI’s documentation describes several routes with different divisions of responsibility. Compare them by asking who owns orchestration and state, where tool code runs, how much integration work is required, and whether the chosen model and runtime support the needed features.
- Responses API: Manage the model-and-tool loop through API requests and application code. This gives the application a direct role in continuing the interaction and handling tool results.
- Agents SDK: Use reusable agents and handoffs when an application benefits from an SDK-managed agent workflow.
- Managed Agents API: Use a managed route where the service takes on more of the agent runtime responsibilities.
- Remote MCP and other tools: Connect tools through supported mechanisms such as remote MCP; built-in tools and tool search can also extend what is available to a model.
These routes are not interchangeable. Their orchestration, state handling, execution locations, and supported capabilities differ. Check OpenAI’s agents guide and OpenAI’s tools guide for the current details and compatibility of a particular configuration.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Where should authorization and safety checks live?
Enforce authorization and business rules in the application or configured service that executes the operation. A model-generated tool request is input to that boundary, not proof that an action is permitted. The handler should validate arguments, check the caller’s permissions, and apply the same business rules it would apply to any other request.
- Keep each tool description narrow and make its inputs explicit.
- Validate argument types, allowed values, and relevant permissions in the handler.
- Return a useful success result or error so the model can respond appropriately or recover.
- Require human review for consequential actions, such as operations involving approvals.
OpenAI’s agents guidance discusses guardrails and human review for tools that affect approvals. The practical implication is to place controls at the execution boundary, where the application can enforce them regardless of what the model requests.
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