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 31 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Using the OpenAI Agents SDK, define an initial `Assistant` with a `GPT-4o` model. Create a `tool` definition for scheduling calendar events (e.g., `schedule_calendar_event(title: str, start_time: str, end_time: str, attendees: list[str])`) and for sending short emails (e.g., `send_short_email(recipient: str, subject: str, body: str)`). Your agent should be instructed to act as a proactive executive assistant.
```python
from openai import OpenAI
client = OpenAI()
assistant = client.beta.assistants.create(
name='Executive Assistant',
instructions='You are a proactive executive assistant. Your goal is to manage the user\'s schedule, communications, and information flow efficiently. Always confirm actions before executing, unless explicitly told otherwise.',
model='gpt-4o',
tools=[
{
'type': 'function',
'function': {
'name': 'schedule_calendar_event',
'description': 'Schedules a new event on the user\'s calendar.',
'parameters': {
'type': 'object',
'properties': {
'title': {'type': 'string', 'description': 'Title of the event'},
'start_time': {'type': 'string', 'format': 'date-time', 'description': 'Start time in ISO 8601 format'},
'end_time': {'type': 'string', 'format': 'date-time', 'description': 'End time in ISO 8601 format'},
'attendees': {'type': 'array', 'items': {'type': 'string'}, 'description': 'List of email addresses of attendees'}
},
'required': ['title', 'start_time', 'end_time', 'attendees']
}
}
},
# Add send_short_email tool definition here
]
)
print(assistant)
```Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.
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.