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Structured source with 18 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Implement the `PrivacyAuditor` agent's logic to evaluate proposed ad strategies against a given privacy policy. This agent should identify and report violations. Finally, design how Capably would monitor this AutoGen workflow (conceptually, as direct integration may vary) and output a compliance report. You'll need to simulate the Capably integration by ensuring the AutoGen system produces structured outputs that Capably could consume.
```python
# PrivacyAuditor logic (conceptual)
class PrivacyAuditorAgent(autogen.AssistantAgent):
def __init__(self, name, llm_config, privacy_policy):
super().__init__(name, llm_config=llm_config, system_message=f"You are a privacy expert. Evaluate ad strategies against the policy: {privacy_policy}. Report any violations.")
self.privacy_policy = privacy_policy
def check_compliance(self, ad_strategy_proposal: dict, user_topics: list) -> list:
violations = []
# Implement your privacy checking logic here
# e.g., if 'user_topics' were used directly for fine-grained targeting without consent
if "direct_targeting_violation" in ad_strategy_proposal.get("flags", []):
violations.append("Direct user topic targeting without consent.")
# ... more policy checks
return violations
# Integrate Capably by ensuring structured output that can be parsed by an external system.
# For example, agents could write structured JSON logs that Capably would ingest.
```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.
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Prompt diagnostics
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This prompt already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
Multi-Agent Ad Policy Auditor
Develop an advanced multi-agent system using AutoGen to autonomously audit proposed advertising strategies against simulated user conversation data. This system will focus on ensuring compliance with privacy policies and verifying ad claims before deployment. Agents will collaborate to extract topics, propose ad targeting, review privacy implications, and fact-check promotional content, providing explainable insights into their decisions. The challenge emphasizes building robust, privacy-aware AI systems that can operate with human oversight where needed.
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.