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.
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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.
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Structured source with 29 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Initialize your OpenAI Agent using the Assistants API. Define a `TransactionMonitor` agent with a tool called `fetch_transactions` that simulates fetching recent crypto transactions from an `Enterprise Transaction API`. Implement a basic `check_compliance` function that the agent can call. Your agent should be able to process a simple query like 'Are there any suspicious transactions?'
```python
from openai import OpenAI
client = OpenAI()
# Assume client.beta.assistants is available for Assistants API
assistant = client.beta.assistants.create(
name="TransactionMonitor",
instructions="You are an expert financial compliance officer. You use provided tools to monitor transactions for suspicious activity.",
model="gpt-4o",
tools=[
{
"type": "function",
"function": {
"name": "fetch_transactions",
"description": "Fetches recent crypto transactions from the Enterprise Transaction API.",
"parameters": {
"type": "object",
"properties": {
"start_time": {"type": "string"}
},
"required": ["start_time"]
},
},
},
# More tools will go here
],
)
# ... rest of the assistant interaction code
```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.
Autonomous Crypto Compliance Agent
This challenge requires building an advanced autonomous agent focused on financial compliance within the cryptocurrency domain or complex supply chain networks. Utilizing OpenAI's Agent SDK, developers will create a system capable of real-time monitoring of transactions and identifying suspicious patterns indicative of illicit activities or regulatory breaches. The agent will leverage sophisticated tool use and function calling to interact with external data sources and analytical frameworks. The core of the system involves GPT-5-2 for advanced reasoning and orchestrating analytical tasks. It will integrate Darts, a time-series forecasting library, to detect anomalies in transaction volumes or patterns over time. Long-term memory and regulatory context will be managed by a vector database (e.g., Pinecone) storing an extensive knowledge base of financial regulations and compliance policies. The agent will also interact with a simulated Enterprise Transaction API to fetch real-time data. The goal is to develop an intelligent agent that not only identifies potential compliance issues but also provides detailed reports, evidence, and recommendations for further investigation, showcasing a modern, proactive approach to financial crime detection.
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.