Multi-Agent Ad Fraud Detection
This challenge tasks you with building a robust multi-agent system capable of proactively detecting, analyzing, and preventing fraudulent ad placements and scam campaigns within a simulated digital advertising platform. Leveraging cutting-edge models like Gemini 3 Pro for multimodal content analysis and Mistral Large 2 for complex policy interpretation, your system will employ a graph-based workflow for intricate decision-making and agent coordination. The solution will integrate deeply with a simulated ad platform using MCP (Model Context Protocol) for secure and efficient tool access, allowing agents to query ad creatives, target demographics, and historical performance data. Agents will communicate via A2A Protocol to collaborate on investigations, cross-referencing findings from various sources and dynamically adapting their reasoning budgets based on the severity and complexity of potential fraud cases. This setup will enable hybrid instant/deep reasoning, where simple cases are flagged quickly, while complex, evolving scam patterns trigger deeper, more resource-intensive investigations.
What you are building
The core problem, expected build, and operating context for this challenge.
This challenge tasks you with building a robust multi-agent system capable of proactively detecting, analyzing, and preventing fraudulent ad placements and scam campaigns within a simulated digital advertising platform. Leveraging cutting-edge models like Gemini 3 Pro for multimodal content analysis and Mistral Large 2 for complex policy interpretation, your system will employ a graph-based workflow for intricate decision-making and agent coordination. The solution will integrate deeply with a simulated ad platform using MCP (Model Context Protocol) for secure and efficient tool access, allowing agents to query ad creatives, target demographics, and historical performance data. Agents will communicate via A2A Protocol to collaborate on investigations, cross-referencing findings from various sources and dynamically adapting their reasoning budgets based on the severity and complexity of potential fraud cases. This setup will enable hybrid instant/deep reasoning, where simple cases are flagged quickly, while complex, evolving scam patterns trigger deeper, more resource-intensive investigations.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master LangGraph for building stateful DAG agent workflows, including dynamic agent routing and checkpointing for fraud investigations.
Implement MCP-enabled tool integration with a simulated ad platform's APIs, leveraging Gemini 3 Pro to interpret ad creatives and metadata.
Design A2A Protocol communication patterns for 'Ad Auditor' and 'Policy Analyst' agents to share findings and escalate suspicious activities.
Deploy Gemini 3 Pro for multimodal analysis of ad images and text, identifying suspicious patterns indicative of scams or policy violations.
Integrate Mistral Large 2 for deep reasoning over complex ad policies and regulatory compliance, generating detailed audit reports.
Orchestrate hybrid instant/deep reasoning by dynamically adjusting agent 'thinking budgets' based on initial risk assessments, using a vector database for RAG over historical fraud patterns.
[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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