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To turn a Python script into an AI agent, keep its predictable work in ordinary Python and let a language model decide when to call a small set of carefully chosen functions. You do not need an agent framework for every use case: a direct API call can be enough when your application should manage the tool loop and state itself. An agent SDK is useful when you want a runtime to manage tool calls, multi-step turns, guardrails, or handoffs.
What changes when a Python script becomes an AI agent?
A conventional script follows logic you specify. An agent adds a model that can interpret a request, choose among tools you make available, use their results, and continue until it has an answer or needs another action. 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.” OpenAI Agents SDK: Agents
The useful change is model-guided selection or sequencing—not replacing working code wholesale. Keep parsing, arithmetic, file operations, and other predictable tasks in Python. Use the model where interpreting a flexible request or choosing the next step is valuable. If the task needs only one model response and no tool execution or multi-step control, a simpler API request may be the better fit.
How do you decide which Python functions to expose?
Start by drawing a boundary around the script: identify what it can already do deterministically, then decide which decisions genuinely need language understanding. Expose only the functions the model needs to complete the bounded task. Leave internal helpers and unrelated capabilities inaccessible.
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Each tool should have a clear name, a narrow purpose, and constrained inputs. Validate arguments and results in Python; a description to the model is not a substitute for application-side checks. Avoid broad credentials or unrestricted file, network, or shell access. For actions with meaningful consequences, add application-appropriate approval and verification.
The example below follows the Agents SDK function-tool pattern. order_service is illustrative application code, not a built-in SDK object or a tested implementation.
from agents import Agent, Runner, function_tool
@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],
)In this example, the model can request an authorized order lookup, but the function and the surrounding application remain responsible for enforcing access. Keep other script functions private unless the agent has a specific, safe reason to call them.
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How do you make a first agent in Python?
For the OpenAI Agents SDK example, the official quickstart installs the openai-agents package, expects OPENAI_API_KEY to be configured in the environment, defines an Agent, and calls Runner.run from an asynchronous entry point. Follow the live quickstart for current setup and model options: package interfaces and model availability can change. OpenAI Agents SDK quickstart
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsimport 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())This is an adaptation of the documented quickstart pattern, not a claim that the code was executed. First confirm that the minimal agent can complete its task; then add one or two well-designed tools and verify their behavior before expanding access. The quickstart also recommends adding capabilities incrementally.
How does the agent loop work, and where does state go?
An SDK run represents one application-level turn. The runtime can send the request to the model, execute a requested tool, send the result back, and continue. It returns when the agent reaches a final answer without more tool work; a handoff can instead move control to another agent. This is why a run can involve several model and tool steps rather than one response.
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- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
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- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 12% better performance in digital content workloads.
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For later turns, the Agents SDK running guide describes four state strategies. Choose one that fits the application, and avoid combining layers without reconciling their context, which can duplicate history. OpenAI Agents SDK: Running agents
- Application-managed history: retain and pass the run’s
result.historyyourself. - SDK session: use a session to manage conversation history across runs.
- Server-managed conversation: continue using a
conversationId. - Responses API continuation: use a prior
previousResponseId.
If you choose a direct API call instead of the SDK, your application can own the loop, tool dispatch, and state. That offers control over those pieces; the SDK is an option when you want its runtime to manage turns and related features.
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Should you use a direct API call or an agent SDK?
Neither approach is categorically best. Make the choice based on who should own execution and state:
| Approach | Choose it when | Your application or runtime owns |
|---|---|---|
| Direct API call | The workflow is short-lived and you want control of the orchestration. | Your application handles the loop, tool dispatch, and state. |
| Agents SDK | You want a runtime to manage turns, tools, guardrails, handoffs, or sessions. | The SDK supplies runtime behavior for the features you choose to use. |
These approaches can coexist in one application. The relevant distinction is not a promised performance advantage; it is which layer should manage the workflow.
When should you add multiple agents?
Begin with one agent until the task presents a concrete need for distinct specialist instructions or routing. Adding agents without that need introduces coordination decisions that a focused tool set may avoid. When delegation is useful, choose how control should flow:
- Agents as tools: a manager calls a specialist for a bounded subtask and remains responsible for combining results and answering the user.
- Handoff: control transfers to a specialist that becomes the active agent and answers the user.
The SDK supports both patterns and allows them to be combined. Use the one that matches who should own the final response, rather than splitting a workflow into agents by default. OpenAI Agents SDK: Multi-agent orchestration
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Guard the actual inputs, outputs, and effects of your tools. OpenAI’s SDK overview describes input and output validation guardrails and built-in tracing; orchestration guidance also recommends monitoring, iteration, and evaluation. OpenAI Agents SDK: Guardrails OpenAI Agents SDK: Tracing
Pay particular attention to privacy and content safety. Inspect traces to understand what failed, then turn observed failures and real edge cases into validations or evaluations. OpenAI’s practical guide recommends refining guardrails as those cases emerge and balancing security with user experience; these practices do not replace authorization or safeguards inside your own Python functions. OpenAI: A practical guide to building agents
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