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Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →To turn a Python script into an AI agent, keep its predictable work in Python and let a language model decide when to call a small set of approved functions. An agent is a model configured with instructions and tools, plus a runtime that can execute those tools and continue until it has a response. If your task needs only one model answer and no tool execution or multi-step control, a direct API call may be simpler.
What changes when you turn a Python script into an AI agent?
A conventional script follows logic you specify: it parses inputs, applies rules, calls functions, and returns a result. An agent adds model-guided decisions about what to do next. OpenAI’s Agents SDK documentation defines an agent as “a large language model (LLM) configured with instructions, tools, and optional runtime behavior such as handoffs, guardrails, and structured outputs.”
The important change is not replacing Python with an LLM. It is allowing the model to select and sequence certain Python functions when a task calls for them. Keep deterministic work—such as calculations, parsing, and predictable file operations—in ordinary Python unless there is a specific reason to change it.
How do I turn a Python script into an AI agent?
1. Identify what should remain deterministic
Trace the script from input to output. Mark which steps require judgment or flexible interpretation, and which steps already have reliable rules. A model may help interpret a request or decide which operation to perform; Python should still do the operation itself when the result is predictable.
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For example, an order-support script might keep authentication and status lookup in existing Python services, while the model interprets the user’s question and decides whether to call a narrowly defined order-lookup function. Do not expose every internal function merely because it exists.
2. Start with one agent and one bounded task
The OpenAI Python quickstart uses the openai-agents package, an API key configured in the environment, an Agent, and Runner.run inside an asynchronous entry point. This adapted example shows the initial shape before adding tools:
import asyncio
from agents import Agent, Runner
agent = Agent(
name="Task assistant",
instructions="Help with the bounded task. Use available tools when needed.",
)
async def main():
result = await Runner.run(agent, "Describe the task here")
print(result.final_output)
if __name__ == "__main__":
asyncio.run(main())Install the current package and configure OPENAI_API_KEY as described in the official quickstart. Check that page for current model names and availability rather than relying on a hard-coded choice from an older example. Get one bounded task working before adding capabilities.
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3. Expose selected Python functions as tools
A tool is a function the model can request the runtime to execute. The SDK quickstart demonstrates decorating a Python function with @function_tool and including it in the agent’s tools list. Keep tools focused, describe their purpose clearly, constrain their inputs, and validate the work they perform.
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@function_tool
def lookup_order(order_id: str) -> str:
"""Return the status of one order the current user may access."""
return order_service.status_for_authorized_user(order_id)
agent = Agent(
name="Order helper",
instructions="Use lookup_order to check an order. Do not invent a status.",
tools=[lookup_order],
)This is illustrative code: order_service represents an existing application service and is not defined here. In a real application, the service must enforce authorization rather than trusting the model or the function description to decide which records a user may access.
- Expose only functions needed for the task; keep unrelated helpers internal.
- Use constrained parameters and validate tool inputs and results.
- Avoid broad credentials or unbounded file, network, and shell access.
- For consequential actions, add application-appropriate checks or human approval before execution.
4. Understand the tool-use loop
A run is one application-level turn. The runtime can ask the model what to do, execute a requested tool, pass the result back to the model, and continue until the agent returns a final answer or transfers control. This is why a tool-enabled agent differs from a single request that simply returns generated text: the application must account for tool execution and its effects.
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For the Agents SDK, see the running agents guide for how runs and continuations work. Design your own application logic so tool failures, invalid parameters, and unsuccessful operations produce controlled results rather than being treated as successful outcomes.
5. Choose how to preserve state across turns
A single run handles a turn; a later turn may need prior conversation context. The SDK’s running guide describes four approaches. Choose the one that fits the application and avoid layering multiple approaches without reconciling what context each one stores.
| State approach | How it works |
|---|---|
| Application-managed history | Keep and pass conversation history, such as result.history, from your own application. |
| SDK session | Use an SDK session to manage conversation state across runs. |
| Server-managed conversation | Continue a conversation using a conversationId. |
| Prior Responses API response | Continue from a previous response using previousResponseId. |
These approaches differ in where and how state is managed; select deliberately based on your application’s needs. Avoid passing the same context through multiple state mechanisms unless you understand how that affects the conversation.
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6. Add validation and observability before expanding access
Protect both the inputs the agent receives and the effects its tools can cause. OpenAI’s SDK overview describes input and output guardrails and built-in tracing. The practical guide to building agents emphasizes privacy and content safety, and recommends refining guardrails as real-world edge cases and failures appear.
Inspect traces to understand which tools were requested and how the run progressed. Turn observed problems into tests or evaluations: for example, check that a user cannot retrieve another person’s order, that malformed identifiers are rejected, and that a failed lookup is not presented as a confirmed status. Increase tool permissions only when the task requires them and the relevant checks are in place.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Should I use a direct API call or an agent SDK?
| Choose | When it fits |
|---|---|
| Direct API call | The workflow is short-lived, and your application should own the loop, tool dispatch, and state. |
| Agents SDK | You want a runtime to manage agent turns, tools, guardrails, handoffs, or sessions. |
These choices can coexist in one application. The right starting point depends on how much orchestration your task needs; the available documentation does not establish that one option is categorically better or faster.
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When should I use multiple agents?
Start with one focused agent and add specialists only when a workflow has a concrete need for separate instructions or routing. The Agents SDK orchestration guide describes two patterns:
- Agents as tools: A manager calls a specialist for a bounded subtask, then remains responsible for combining results and giving the final answer.
- Handoffs: The active agent transfers control to a specialist that takes over the response.
Use a manager when one agent needs to own the final answer across specialist work; use a handoff when the specialist should become responsible for the next response. The patterns can be combined, but they add coordination choices that a small script conversion may not need. See the orchestration guide for their SDK behavior.
What should I check before putting the agent into use?
- Is the task bounded, with a clear expected result?
- Are predictable operations still handled by ordinary Python?
- Does each exposed tool do one useful job with constrained inputs?
- Do the application’s services enforce authorization and validate effects independently of the model?
- Have you selected one coherent state strategy for multi-turn work?
- Can you inspect runs and convert observed failures into evaluations or checks?
Code interfaces and model availability can change. The examples here follow official OpenAI documentation checked on October 4, 2026; consult the linked live documentation for current setup details. The code is an adaptation of documented patterns, not a claim of execution or independent testing.
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