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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →An AI agent is not just a prompt sent to a model. In the OpenAI Agents SDK for TypeScript, an agent combines a model with instructions and can also be equipped with tools or handoffs. A runner interprets the model’s responses, carries out requested actions or transfers control, and repeats until the agent produces a final answer or the run reaches a configured limit.
That is an implementation-oriented explanation based on this SDK, not a universal formal definition: systems described as agents can be built and orchestrated in different ways.
What makes an AI agent different from a prompt?
A prompt gives a model input. An agent is a configured participant in a process: it has a model and instructions, and may have tools it can call or handoffs that let it transfer control to another agent. The OpenAI Agents SDK describes its own framing this way: “An agent is an LLM equipped with instructions, tools and handoffs.” OpenAI Agents SDK overview
Instructions are the directions supplied as part of an agent definition; the SDK’s guide describes them as that agent’s system prompt. They shape how the agent should respond, but they do not, by themselves, make external actions happen. Agents guide
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The runner is the part that turns model responses into an ongoing interaction. As the SDK documentation puts it, “Agents do nothing by themselves – you run them with the Runner class or the run() utility.” Running Agents guide
How does the agent loop work?
The model does not directly perform every action it describes. It can return a tool call or a handoff; the runner interprets that result, carries out the corresponding work, and continues the run.
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- The runner calls the current agent with the conversation.
- If the response is final output, the runner returns it.
- If the response requests a tool, the runner executes that tool, adds its result to the interaction, and calls the model again.
- If the response is a handoff, the runner switches to the receiving agent and continues.
In pseudocode, the flow looks like this:
current agent = starting agent
repeat:
response = call current agent with conversation
if response is final output: return it
if response is handoff: switch current agent
else if response contains tool calls: execute them and append resultsThis is a description of the SDK runner’s flow, not a claim that the pseudocode is a complete runner implementation. The SDK can also stop with an exception if its configured maximum turn count is exceeded. That limit is SDK control behavior, not a requirement for every agent architecture. Runner API
Build a minimal TypeScript agent
The SDK’s basic example defines an agent and runs it with a user message:
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import { Agent, run } from '@openai/agents';
const agent = new Agent({
name: 'Assistant',
instructions: 'You are a helpful assistant',
});
const result = await run(agent, 'Write a haiku about recursion in programming.');
console.log(result.finalOutput);The string passed to run() is treated as a user message. The runner invokes the starting agent, then handles any tool calls or handoffs before returning final output. In an existing TypeScript app, the SDK quickstart uses an index.ts entry point as its example setup. TypeScript quickstart
This minimal example needs no tool or second agent: those are optional capabilities, not prerequisites for using the SDK’s agent-and-runner pattern.
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What is a tool call?
A tool is a callable capability through which an agent can request an action. The model proposes the call; the runner or relevant SDK component executes it and makes the result available in the interaction so the model can continue. A tool call therefore differs from plain text that merely says an action was taken.
The SDK groups several kinds of tools, including hosted tools, built-in execution tools, function tools, agents exposed as tools, MCP servers, and sandbox capabilities. Which kind is appropriate depends on what the application needs the agent to do. Tools guide
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Tool call or handoff: who stays in control?
The key difference is control ownership. With a tool call, the current agent remains the one the runner is using; with a handoff, control moves to the receiving agent for the continuing conversation.
| Pattern | Who owns control? | How the specialist participates | Who produces the final response? |
|---|---|---|---|
| Manager pattern | The central manager agent remains in control. | A specialist is exposed as a tool and performs a bounded callable task. | The manager can incorporate the specialist’s result and produce the response. |
| Handoff pattern | Control transfers to the receiving agent. | The receiving specialist takes over the conversation. | The receiving agent can continue and produce the final response. |
A handoff carries conversation context to the receiving agent unless filtering changes what is passed along. These are orchestration choices, not competing definitions of an agent. Agent orchestration guide
What an agent does not have to include
The SDK’s model supports optional tools and handoffs; it does not imply that every agent needs multiple tools, multiple agents, persistent memory, planning, or long-running autonomous execution. A simple agent may answer a single request, while a more involved application may give it tools or delegate work. Choose the orchestration that fits the task rather than adding capabilities merely to make a system look more agent-like.
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