EU DMA/DSA Policy Compliance Graph Agent
Create a graph-based multi-agent system using LangGraph and Claude Sonnet 4 to assist enterprises in navigating the complexities of EU Digital Markets Act (DMA) and Digital Services Act (DSA) compliance, given the intensified enforcement. This challenge requires building agents that can interpret intricate legal texts, assess company practices against regulations, and identify potential compliance gaps. The system will employ an A2A protocol for agents to communicate and collaborate on different aspects of regulatory analysis. MCP tool integration will provide agents with access to internal company policies, platform usage data, and legal databases. Optuna will be used to optimize prompt engineering and agent workflow parameters, ensuring the most accurate and efficient interpretation of legal documents. The final output will be a detailed compliance report and actionable recommendations for mitigating risks.
What you are building
The core problem, expected build, and operating context for this challenge.
Create a graph-based multi-agent system using LangGraph and Claude Sonnet 4 to assist enterprises in navigating the complexities of EU Digital Markets Act (DMA) and Digital Services Act (DSA) compliance, given the intensified enforcement. This challenge requires building agents that can interpret intricate legal texts, assess company practices against regulations, and identify potential compliance gaps. The system will employ an A2A protocol for agents to communicate and collaborate on different aspects of regulatory analysis. MCP tool integration will provide agents with access to internal company policies, platform usage data, and legal databases. Optuna will be used to optimize prompt engineering and agent workflow parameters, ensuring the most accurate and efficient interpretation of legal documents. The final output will be a detailed compliance report and actionable recommendations for mitigating risks.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Design and implement graph-based agent workflows using LangGraph, defining nodes for 'Legal Document Parser', 'Compliance Assessor', 'Risk Identifier', and 'Recommendation Generator' agents for DMA/DSA analysis.
Implement A2A (Agent-to-Agent) protocol using LangGraph's message passing capabilities, ensuring secure and efficient communication between compliance agents to share findings and converge on recommendations.
Leverage Claude Sonnet 4 for its strong performance in complex document understanding and summarization, specifically for parsing legal texts like the DMA and DSA, and extracting relevant obligations.
Utilize Optuna for optimizing prompt engineering strategies and agent workflow parameters, such as retry logic or parallel processing nodes, to maximize accuracy and minimize latency in compliance assessments.
Build MCP-enabled tool integrations using Semantic Kernel (or a custom solution) to connect agents with internal company policy databases, platform analytics APIs (e.g., usage statistics), and external legal research platforms (e.g., Westlaw or LexisNexis APIs).
Generate a comprehensive EU compliance report, including identified non-compliance points, severity ratings, and specific, actionable recommendations for remediation tailored to a hypothetical 'Big Tech' company.
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[ok] Wrote eval/examples.json
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