Generative AI Regulatory Compliance with LangGraph and Claude Sonnet 4.5
Challenge requires building a sophisticated agent system for automating the analysis of new AI projects against complex and evolving regulatory landscapes. The goal is to develop a robust, graph-based workflow that can ingest project specifications and relevant legal documents, identify compliance risks, and generate actionable recommendations and detailed compliance reports. Participants will utilize LangGraph to create a stateful, DAG-based multi-agent system, orchestrating agents like a 'Policy Interpreter,' 'Project Risk Analyzer,' and 'Compliance Report Generator.' The system will leverage Claude Sonnet 4.5 for efficient, high-throughput parsing and summarization, and GPT-5 for deep legal reasoning on edge cases. MCP-enabled tool integration will be crucial for connecting to enterprise document management systems and regulatory databases.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a LangGraph-based agent system using Claude Sonnet 4.5 and GPT-5 for automated, enterprise-grade regulatory compliance analysis with MCP tool integration.
How work is evaluated
The evaluation will assess the system's ability to accurately identify compliance risks in AI projects, provide justified recommendations, and generate comprehensive, well-structured compliance reports based on the given regulations and project details.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Integrate Claude Sonnet 4 for efficient initial policy interpretation and document summarization.
Develop a system for automated generation of detailed compliance reports and actionable recommendations.
Leverage GPT-5 for advanced legal reasoning and risk assessment in challenging compliance scenarios.
Implement MCP-enabled tool integration for seamless access to enterprise legal documents and regulatory databases.
Design and implement graph-based agent workflows using LangGraph for complex compliance analysis.
Reference links and supporting material
Sample data for 2 tasks
How this agent runs
The evaluation will assess the system's ability to accurately identify compliance risks in AI projects, provide justified recommendations, and generate comprehensive, well-structured compliance reports based on the gi...
Challenge input
JSON object: {'project_description': 'string', 'regulation_text': 'string'}
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
JSON object: {'identified_risks': [{'risk_type': 'string', 'severity': 'low|medium|high', 'justification': 'string', 'relevant_clauses': ['string']}]}
- Verify the LangGraph workflow executes successfully without deadlocks or errors.
- Confirm successful invocation and data retrieval from simulated enterprise tools via MCP.
- RiskIdentificationF1Score target: 0.88
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- The evaluation module defines the checks.
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.