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AI agents are AI systems that can work through a task by choosing from tools connected to them—such as web search, file retrieval, application functions, or a computer interface. The AI model can request an action, but the surrounding application supplies and runs the tool, returns its result, and sets the agent’s access. An agent can retrieve information, update systems, communicate, or coordinate multi-step work only when its tools and permissions allow it.
What is an AI agent?
There is no universal, standards-body definition of an AI agent established by the sources cited here. OpenAI describes agents as “systems that independently accomplish tasks on behalf of users.” That is the company’s working description, not a formal industry standard. A practical distinction is that a text assistant mainly generates a response, while an agent can use a configured tool, inspect the result, and continue with another step. “Independently” does not mean unrestricted access or guaranteed success: developers configure the tools and execution environment. OpenAI’s March 11, 2025 announcement and its function-calling guide describe these ideas.
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What can AI agents do with connected tools?
The available tools define what an agent can attempt. OpenAI’s agent-building guide groups tools into data, action, and orchestration categories; in practice, a workflow may combine them.
Find and retrieve information
An agent can search the web, inspect uploaded files or PDFs, or query connected records such as a CRM or transaction database. Retrieved information gives the model material to use in an answer or a later step. OpenAI’s tools guide describes tool integrations.
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Use application functions and APIs
A developer can expose an application function through a structured tool definition. The model may request that function with arguments; application code then runs it and returns the result. Depending on how the function is built, it can retrieve data or take an action. The model’s request is not itself execution: the application controls what happens. See the function-calling guide.
Change records or communicate
Tools can be designed to update a record, send an email or text, or route a customer-service ticket to a human. These are examples of possible integrations, not abilities every agent has. Because such actions can affect people and business data, the application’s tool configuration and execution environment are important parts of the workflow. OpenAI’s agents guide discusses tool-enabled workflows.
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Operate a computer interface
Computer-use tools let an agent interact with a configured browser or other interface—for example, to gather information from a website, test a site, or use an application through its UI. This can help with software that lacks an API, but the agent’s reach depends on the browser and runtime it has been given. OpenAI’s computer-use guide describes this approach and browser origin approval in its workflow; that detail should not be read as a claim that every agent action everywhere requires user approval.
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Depending on the tools and execution environment, an agent may run code, edit files, use MCP connections, or produce files. These capabilities are described for OpenAI’s Agents API in its Agents API FAQ; they are not automatic features of every agent.
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Coordinate work across agents
An agent can use a specialist agent as a tool, for example by delegating research, writing, or support work. This is an orchestration pattern, not a guarantee of quality: a workflow with several tools or agents is more complex than a single-tool interaction and still depends on the tools, instructions, and results involved. OpenAI covers orchestration in its agents guide and agent-building announcement.
How does an agent use a tool?
A typical function-calling workflow has five stages:
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- The application sends the model a request along with the tools it is allowed to use.
- The model decides whether a tool is useful and, if so, returns a tool call with the requested arguments.
- The application executes the requested function or tool.
- The application sends the tool’s result back to the model.
- The model returns an answer or requests another tool, repeating the cycle if needed.
The model chooses or proposes a tool call; the application executes it and supplies the result. Developers configure the model, instructions, tools, and output controls. Depending on the workflow, execution may also require an API connection, MCP server, browser, or sandbox. The function-calling guide explains the request-and-return cycle.
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An agent cannot use an application capability that has not been exposed to it. Its practical limits are set by the available tools, the permissions and instructions around them, and the environment in which those tools run. A system that can only search and read has different consequences from one that can also alter records or send messages.
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For an agent that uses a computer interface, the configured browser and runtime determine what it can reach. For other tools, the application’s implementation determines what a requested function actually does. These are deployment choices, not inherent powers of the model. OpenAI’s agents guide and computer-use guide describe these implementation considerations.
What should you check before deploying one?
- Connected systems: Identify which sites, files, APIs, databases, or applications the agent can access.
- Read versus action permissions: Distinguish tools that retrieve information from tools that can change records or communicate.
- Interaction method: Determine whether the agent uses an API or MCP connection, or operates through a user interface.
- Execution environment: Check the browser, runtime, or sandbox in which tools run and what that environment permits.
- Data terms: Verify the current retention and residency terms for the specific product, plan, and region rather than assuming they apply across vendors or products.
For the Agents API beta covered by OpenAI’s Help Center FAQ at the time that FAQ was documented, OpenAI said data residency was supported in the United States only and Zero Data Retention was not supported, including when using a self-hosted sandbox. These are product-specific, changeable terms; consult the current FAQ for the exact product and region before relying on them.
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