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Implement Patronus AI Evaluation & Wix Dashboard
Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.
Linked challenge: Ethics Compliance Agent for Financial Market Analysis
Format
Code-aware
Lines
35
Sections
1
Linked challenge
Ethics Compliance Agent for Financial Market Analysis
Prompt source
Original prompt text with formatting preserved for inspection.
35 lines
1 sections
No variables
1 code block
Integrate Patronus AI into your AutoGen workflow to continuously evaluate the 'Legal Compliance Officer' agent's ethical reasoning and compliance adherence. Set up a logging mechanism that captures agent conversations and decisions (e.g., detected violations, reasoning), then feed these logs to Patronus AI for automated policy checks and bias detection. Concurrently, design a Wix dashboard that visually represents these evaluation results, agent activities, and the overall compliance status of analyzed scenarios.
```python
# This example is conceptual; actual integration would involve Patronus AI SDK and Wix Velo. # import patronus_ai
# import json # For logging data # patronus_client = patronus_ai.Client(api_key='YOUR_PATRONUS_API_KEY') # After an agent's decision, capture relevant data:
# def log_and_evaluate_agent_decision(agent_name, prompt, response, decision_data):
# log_entry = {
# 'agent_id': agent_name,
# 'prompt': prompt,
# 'response': response,
# 'decision': decision_data,
# 'timestamp': datetime.now().isoformat()
# }
# # Send to Patronus AI for evaluation against a predefined policy
# try:
# evaluation_result = patronus_client.evaluate_policy(policy_id='ethical-compliance-policy-v1', data=log_entry)
# print(f"Patronus AI Evaluation for {agent_name}: {evaluation_result}")
# # You would then push these results to a database accessible by Wix
# except Exception as e:
# print(f"Error sending to Patronus AI: {e}")
# return log_entry # In your AutoGen agent function (e.g., LegalComplianceOfficer's reply function):
# response_content = legal_compliance_officer.generate_reply(messages=messages_history)
# compliance_decision = parse_compliance_from_response(response_content)
# log_and_evaluate_agent_decision("LegalComplianceOfficer", messages_history[-1]['content'], response_content, compliance_decision) # For Wix integration, you would use Wix Velo backend functions to receive data
# (e.g., via a webhook) and update dataset collections or display dynamic content.
# Example Wix Velo code (conceptual):
# import wixData from 'wix-data';
# export function myHttpFunction(request) {
# if (request.method === 'POST') {
# let body = JSON.parse(request.body);
# wixData.insert('ComplianceReports', body)
# .then((results) => { return ok({inserted: true}); });
# }
# return badRequest('Method not allowed');
# }
```Adaptation plan
Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.
Keep stable
Preserve the rubric, target behavior, and pass-fail criteria as the baseline for evaluation.
Tune next
Adjust fixtures, mocks, and thresholds to the system under test instead of weakening the assertions.
Verify after
Make sure the prompt catches regressions instead of just mirroring the happy-path examples.