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An AI model can decide to use a tool without performing the external action itself. It returns a structured request—such as a tool name and arguments—and the surrounding application, often called the runtime or orchestrator, validates and executes that request. The runtime sends the result back, and the model can then decide what to do next.
What “the model decides; it does not do” means
In a tool-using system, the model produces decisions and instructions as output. A separate runtime connects those outputs to real effects, such as calling an API, querying a database, retrieving a document, or operating a computer. As author Bhavya Khatri puts it in a chapter on agent design, “The model decides; it does not do” (AI Agents). This is a useful explanation of the division of labor, not a formal standards rule.
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The distinction matters because a generated request is not proof that an action happened. The application must receive the request and choose whether and how to carry it out. OpenAI’s function-calling documentation describes this tool-call and execution cycle.
What happens after an agent chooses a tool?
- The application defines the options. It gives the model a task and descriptions of available tools, often with schemas that constrain acceptable arguments.
- The model responds. It can answer directly, or return a structured request naming a tool and supplying arguments. The request is a proposal for an operation, not the operation itself.
- The runtime checks and dispatches it. The surrounding application can validate the arguments, check identity and permissions, apply policy, and invoke the corresponding service or computer-use environment.
- The runtime returns an observation. It passes the operation’s result—such as retrieved information or an error—back to the model.
- The model continues or finishes. It can use the observation in a final answer, request another tool, or take another step in the loop.
This repeated cycle of decision, action, and observation is what makes many agent workflows more than a single model response. The OpenAI announcement about tools for building agents describes its own agent-building features; exact product availability and configuration can change.
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Does an AI agent ever click?
Yes, at the system level. A computer-use tool can take proposed mouse or keyboard actions from a model and translate them into commands in an environment. The runtime or tool performs those commands; the model supplies the proposed action. OpenAI describes this arrangement in its computer-use tooling announcement.
So “the model never clicks” is shorthand for the boundary between proposing an action and executing it—not a claim that agent systems cannot operate a browser or computer. Nor does every agent use clicks: many call APIs, search data, or retrieve files instead.
Where responsibility and safeguards belong
The runtime is where a system can enforce operational limits, but its presence does not make an agent automatically safe. Developers must decide what controls to implement and how strictly to apply them. Useful safeguards include:
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- Validate requests: Check that tool names and arguments match the expected schema and reject malformed or out-of-scope requests.
- Enforce access: Confirm identity and permissions before allowing access to an account, service, file, or device.
- Limit consequences: Apply policy and action limits; require human approval for consequential operations where appropriate.
- Handle returned content as data: Retrieved pages and tool results may contain instruction-like text. The system should not treat that content as privileged instructions that override its rules.
- Make the loop reviewable: Record what was requested, what the runtime allowed, and what result came back, with human inspection or approval at suitable points.
These are implementation choices, not guaranteed properties of anything labeled an agent. The agent-design chapter discusses the runtime’s role in checking and dispatching calls, while OpenAI’s function-calling guide explains the developer-side execution flow.
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The word “agent” alone does not tell you what a system can do or how much oversight it has. To understand its behavior, check:
- Tool scope: Which APIs, browser actions, files, or other capabilities are available?
- Call structure: Are tool names and arguments constrained by schemas? Can the model request multiple tools in one turn?
- Execution boundary: Which component receives requests, dispatches them, and returns observations?
- Safeguards: How are arguments, identity, permissions, and policy limits checked? Which actions require approval?
- Loop and review: How many steps can run before the system returns control, and where can a person inspect or approve the outcome?
- Observation handling: How does the system keep external content from being mistaken for instructions with authority?
These questions reveal more than the label: they show what the model is allowed to propose, what the application is willing to execute, and where a person can intervene.
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