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 33 active lines to adapt.
Already linked to a challenge workflow.
Sign in to keep private prompt variations.
Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Initialize AutoGen and define the core agents for the system: a 'UserTopicAnalyzer' (using Gemini 2.5 Flash), an 'AdStrategist', a 'PrivacyAuditor', and a 'FactChecker'. Configure their roles, system messages, and communication patterns. Consider using `ConversableAgent` for custom logic and `AssistantAgent` for LLM integration.
```python
import autogen
config_list = [
{
"model": "gemini-2.5-flash",
"api_key": "YOUR_GEMINI_API_KEY",
"api_type": "google",
"base_url": "https://generativelanguage.googleapis.com/v1beta",
}
# Add other LLM configurations as needed
]
llm_config = {"config_list": config_list, "temperature": 0.7}
# Define agents
user_proxy = autogen.UserProxyAgent(
name="admin",
human_input_mode="TERMINATE",
max_consecutive_auto_reply=10,
is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
code_execution_config={"last_n_messages": 3, "work_dir": "coding"},
)
user_topic_analyzer = autogen.AssistantAgent(
name="UserTopicAnalyzer",
llm_config=llm_config,
system_message="You are an expert at analyzing user conversation data to extract primary topics and interests. Use Gemini 2.5 Flash to quickly identify key themes for ad targeting.",
)
ad_strategist = autogen.AssistantAgent(
name="AdStrategist",
llm_config=llm_config, # Or a different LLM config
system_message="You are an ad campaign manager. Based on user topics, propose a concise ad strategy, including title, target audience, and key phrases. Await privacy review.",
)
# ... continue defining PrivacyAuditor and FactChecker agents
```Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.
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.
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.