To combine async Python, AI agents, and Pydantic, run the agent workflow inside an async entry point, await its operations, and validate data at boundaries such as model outputs, tool arguments, and handoff payloads. Asyncio coordinates waiting work; Pydantic checks whether data matches a declared shape. Neither makes generated claims true or grants permission to take an action.
How async Python fits an AI-agent workflow
An async def statement defines a coroutine function. Calling that function creates a coroutine object; it does not, by itself, schedule the work. The coroutine runs when it is awaited, passed to asyncio.run() at the top-level entry point, or scheduled as a task. Python’s documentation calls async/await coroutines the preferred way to write asyncio applications: Python 3.14.7 asyncio documentation.
Asyncio uses cooperative scheduling: the event loop runs one task at a time, and when a task awaits an operation, other tasks can make progress. This is useful for overlapping I/O waits, such as independent network requests. It does not automatically run Python code in parallel across CPU cores, and CPU-heavy work will not become faster simply because it is placed in an async function.
Start with an awaited runner
The OpenAI Agents SDK exposes asynchronous Runner.run(), as well as synchronous and streaming alternatives. A minimal shape for an async application is:
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import asyncio
from agents import Agent, Runner
agent = Agent(
name="Helper",
instructions="Answer the user's question clearly.",
)
async def main():
result = await Runner.run(agent, "Explain async Python in one sentence.")
print(result.final_output)
if __name__ == "__main__":
asyncio.run(main())This example shows the execution pattern; it does not define a Pydantic output schema. Consult the SDK’s running agents guide for the current runner interface and options.
Choose sequential or concurrent work based on dependencies
Await operations sequentially when a later step depends on an earlier result. If two operations are independent and spend time waiting on I/O, schedule them as concurrent tasks so their waits can overlap. Concurrency introduces more complex failure and cancellation behavior, so it is not a benefit when the work must happen in order.
| Approach | Use it when | Trade-off |
|---|---|---|
Sequential await |
The next operation needs the previous result, or a simple order is easiest to reason about. | Independent waits do not overlap. |
| Concurrent tasks | Operations are independent and can make progress while awaiting I/O. | You must decide how related tasks are tracked, cancelled, and reported when something fails. |
Use TaskGroup for related task lifetimes
asyncio.TaskGroup, added in Python 3.11, provides structured task lifetime management: the context waits for its tasks before exiting. In the documented failure case, if a task fails, the group cancels the remaining tasks and propagates failures using exception-group behavior. This is often a good fit for a set of related operations that should succeed or fail together. Review the Python asyncio task documentation for behavior applicable to your Python version.
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Use gather when its failure behavior suits the workflow
asyncio.gather() is another way to await multiple operations and is used in the Agents SDK orchestration guide for parallel work. Do not treat it as interchangeable with TaskGroup: task lifetime and failure handling differ. Choose based on whether remaining work should continue or be cancelled after a failure, and check the semantics in the documentation for the Python version you deploy.
When using asyncio.create_task() outside a TaskGroup, keep a reference to each task and await or otherwise manage it. Python’s documentation warns that the event loop retains only weak references to tasks, so an unreferenced task can disappear before it finishes.
Choose who controls the agent workflow
An agent is more than a single model call: the OpenAI Agents SDK describes agents in terms of instructions, tools, and optional runtime behavior such as handoffs, guardrails, and structured outputs. The runner can manage turns and related SDK behavior, or your application can orchestrate steps directly with Python code. The appropriate choice depends on how much control you need over workflow and state.
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| Choice | Best fit | Trade-off |
|---|---|---|
| SDK-managed runner | You want the SDK’s run and agent workflow facilities, including tools and handoffs where configured. | Your application follows the SDK’s runner and orchestration model. |
| Code-managed orchestration | You need explicit control over step ordering, branching, state, or parallel independent work. | You own the workflow logic and its error handling. |
The SDK documents async, synchronous, and streaming runner variants in its running agents guide and describes orchestration options in its documentation. Asyncio and an agent runner solve different problems: asyncio coordinates Python tasks, while the runner provides an agent execution interface.
Use Pydantic to make data boundaries explicit
Pydantic models declare fields and types, then validate data against that schema and report validation errors. In an agent application, explicit models are useful wherever loosely structured data crosses into application logic: structured model output, tool parameters, handoff inputs, and external data. See the Pydantic models documentation.
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Example: validate structured output
Define the shape the application expects, then pass it as the agent’s output type:
from pydantic import BaseModel
from agents import Agent
class Answer(BaseModel):
response: str
needs_follow_up: bool
agent = Agent(
name="Structured helper",
instructions="Respond with an answer and indicate whether follow-up is needed.",
output_type=Answer,
)The model provides a typed result contract rather than requiring the rest of the application to interpret arbitrary prose. Handle validation or run failures explicitly; do not assume every input or model response will satisfy the declared shape.
Example: validate handoff input
A handoff can carry a typed payload to another agent or callback. The SDK’s handoff guide demonstrates a Pydantic input model and local validation of returned JSON before passing it to the callback. This puts a check at the transition point instead of relying on every caller to construct correct data. See the handoffs guide.
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What validation does—and does not—guarantee
A successful Pydantic validation means the data meets the declared schema and validators. It does not establish that a generated statement is factually correct, that a requested action is authorized, or that an input is safe for every downstream use. Use separate application logic for factual checks, authorization, and policy decisions.
When validation fails, treat the failure as part of the workflow: capture the relevant error, decide whether to reject the result, ask for corrected data, or return a safe failure to the caller. Avoid silently coercing or discarding values in ways that change the meaning of the request.
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Practical implementation checklist
- Use a supported Python version and check version-specific asyncio behavior;
TaskGrouprequires Python 3.11 or later. - Call
asyncio.run(main())at a conventional top-level script entry point, and useawaitfor coroutines inside async code. - Use concurrency for independent I/O-bound work, not as a substitute for CPU parallelism or required sequencing.
- Prefer a
TaskGroupwhen related tasks should share a managed lifetime and its cancellation behavior fits the workflow. - Declare Pydantic schemas for the data your application relies on, especially at model, tool, handoff, and external-data boundaries.
- Handle schema failures and agent-run failures deliberately; schema validation is not a substitute for truth checks or authorization.
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