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 19 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Implement the 'Client Vetting Agent' using the OpenAI Agents SDK. This agent should be capable of:
1. Receiving client information (refer to `SimulatedClientVetting` input_format).
2. Calling a mock enterprise API to 'verify identity' and 'check financial history'.
3. Querying Weaviate for stored compliance rules and past client flags.
4. Making a decision on client approval and identifying any compliance flags.
Ensure secure handling of mock data and demonstrate tool use within the OpenAI Agents SDK. Include basic agent initialization like:
```python
from openai import OpenAI
client = OpenAI()
assistant = client.beta.assistants.create(
name="ClientVettingAgent",
instructions="You are an expert financial compliance officer. Your role is to vet new clients and ensure they meet all regulatory requirements.",
model="gpt-4o",
tools=[
{"type": "function", "function": {"name": "verify_identity", "description": "Verify client identity...", "parameters": {}}},
{"type": "function", "function": {"name": "check_financial_history", "description": "Check client's financial background...", "parameters": {}}}
]
)
```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.
Secure Enterprise Financial Automation
Develop an autonomous agent system using OpenAI Agents SDK to automate complex financial operations within an enterprise setting. This challenge requires building a multi-agent orchestration layer capable of interacting with various financial data sources and enterprise APIs securely. The system must demonstrate reliable execution of tasks such as client vetting, transaction processing, or trade automation, while ensuring strict compliance and audibility. Performance and reliability will be evaluated using adaptive experimentation principles.
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.