Develop Privacy Auditor Logic and Capably Reporting

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.
```