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An AI model can decide to use a tool without carrying out the action itself. It returns a structured request—such as a function name and arguments—and the surrounding application, often called the runtime or orchestrator, validates and executes that request. The result is then sent back to the model. In systems with computer-use tools, the runtime can translate proposed mouse or keyboard actions into executable commands, so the system can click even though the model’s role is to propose what should happen.
What “the model decides; it does not do” means
In a tool-using system, the model produces decisions and requests as output. A separate component performs actions that affect external services, files, or a computer environment. Bhavya Khatri captures this distinction in the chapter [chapter title]: “The model decides; it does not do.” That is a useful design principle, not a formal standards rule.
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The boundary matters because a model’s generated tool request is not proof that an action has happened. A request to send a message, retrieve a record, or click a button only becomes an external action when the application processes it and invokes the relevant capability.
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- The application sets the task and tools. It gives the model instructions and describes which functions or other capabilities are available.
- The model responds. It may answer directly, or return a structured request that names a tool and supplies arguments. The request can be constrained by a schema.
- The runtime checks and dispatches it. The host application can validate the arguments, check identity and permissions, apply policy, and decide whether approval is required. If allowed, it invokes the corresponding function or environment.
- The tool returns an observation. The runtime records the result and passes it to the model.
- The model continues or finishes. It can use the observation in a final answer or request another tool. An agent workflow may repeat this decision, action, and observation cycle.
OpenAI’s function-calling documentation describes the tool-call and execution-result cycle. The runtime’s role is also explained in the agent-design chapter.
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Can an AI agent click a button?
Yes—at the system level. Computer-use tools can capture mouse and keyboard actions proposed by a model and translate them into commands in an environment. The model may determine that a click is the next step, while the computer-use tool and runtime carry it out. OpenAI describes this approach in its agent-building announcement.
Not every agent uses a browser or clicks anything. Its tools might instead call an API, query a database, retrieve a document, or perform another defined operation. “Agent” alone does not tell you which capabilities a system has or how its actions are executed.
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Where do safety checks belong?
The runtime is where a developer can place controls before an action reaches an external system. It is not automatically safe: the safeguards depend on how the application is built.
- Validate requests: Check that tool arguments match the expected schema and reject malformed or out-of-scope requests.
- Enforce authority: Verify identity, permissions, and policy limits before dispatching a call.
- Require review where warranted: Add human approval for consequential actions rather than treating a model’s proposal as sufficient authorization.
- Handle returned content as data: External pages, documents, and tool results may contain instruction-like text. The application should not let such content override its governing instructions.
- Keep an audit trail: Recording requests, checks, actions, and results can help people understand what the system actually did.
These are implementation choices, not guarantees that come with the label “agent.” The agent-design chapter discusses the runtime boundary and safeguards.
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How to assess an agent’s action boundary
To understand what a particular agent can do—and who is responsible for an action—look beyond its model name. Ask:
- What tools are available? Identify the APIs, files, browser actions, or computer controls the system can access.
- How are calls constrained? Check whether tool names and arguments follow schemas and whether multiple calls can be made in one turn.
- Which component executes them? Determine what dispatches requests, connects to external systems, and returns observations.
- What checks happen before execution? Look for argument validation, permission checks, policy enforcement, and approval requirements.
- How long can the loop run? Find out how many steps may occur before the model returns a result and where a person can inspect or intervene.
- How is untrusted content handled? Check whether instructions found in retrieved pages or tool output are kept separate from the system’s controlling rules.
OpenAI’s AI product primer offers broader conceptual context, while the function-calling and agent-building documentation describe specific tool-use patterns. Product features and availability can change, so check the relevant product documentation for the configuration you use.
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