Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 16 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Develop the actual Python functions that back your `schedule_calendar_event` and `send_short_email` tools. For `schedule_calendar_event`, simulate interaction with a calendar API (or use a placeholder list of events). For `send_short_email`, simply print the email details to the console. Then, create an OpenAI `Thread` and test the agent's ability to process a user request that requires tool use, such as scheduling a meeting. Ensure your tool outputs are fed back into the thread.
```python
def schedule_calendar_event(title: str, start_time: str, end_time: str, attendees: list[str]):
# Simulate calendar API call or add to a dummy list
print(f'Simulating calendar event creation: {title} from {start_time} to {end_time} with {attendees}')
return {'status': 'success', 'event_id': 'evt_12345'}
def send_short_email(recipient: str, subject: str, body: str):
# Simulate email sending
print(f'Simulating email to {recipient} with subject "{subject}" and body: {body}')
return {'status': 'success', 'message_id': 'msg_67890'}
# Example of running an assistant thread
# thread = client.beta.threads.create()
# client.beta.threads.messages.create(thread_id=thread.id, role='user', content='...')
# run = client.beta.threads.runs.create(thread_id=thread.id, assistant_id=assistant.id)
# ... manage run status and tool outputs ...
```Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
Build a Proactive Executive Assistant Agent with OpenAI Agents SDK
Inspired by the recent discussion around advanced call-screening features, this challenge tasks you with developing a personalized, proactive AI executive assistant. This agent will autonomously manage communications, prioritize tasks, and synthesize information, effectively acting as a digital chief of staff. It should demonstrate complex reasoning, dynamic tool usage, and an understanding of user preferences to handle various professional scenarios. The core of this challenge involves leveraging the OpenAI Agents SDK to orchestrate a sophisticated agent workflow. You will implement function calling to integrate with external tools for managing schedules, emails, and information retrieval. The agent needs to exhibit nuanced decision-making, adapting its behavior based on the context of incoming communications and the user's current priorities, much like a human executive assistant would filter and manage information flow.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.