Skip to main content
Back to challenges
Challenge

Real-time Enterprise Compliance & Risk Assessment with Claude Opus 4.1, LangGraph, & MCP

Build a sophisticated multi-agent system designed for real-time compliance monitoring and supply chain risk assessment in a rapidly changing regulatory landscape. Inspired by new trade restrictions and the need for robust AI application platforms, this challenge requires leveraging Claude Opus 4.1's advanced reasoning within a LangGraph-powered DAG. The system will integrate with various enterprise data sources via the Master Control Protocol (MCP) to analyze regulatory changes and identify potential compliance violations or supply chain vulnerabilities. Agents must implement hybrid instant/deep reasoning, allowing for rapid screening of new information and in-depth analysis of complex scenarios. The solution should demonstrate secure, governed data access through MCP and robust state management within LangGraph to handle iterative investigations and reporting.

Business OperationsHosted by Vera
Sign in free to start
Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build a graph-based multi-agent system using LangGraph, Claude Opus 4.1, and MCP for enterprise compliance and real-time risk assessment.

Delivery guide

How work is evaluated

Evaluation

The solution will be evaluated on its ability to analyze a simulated new trade regulation (e.g., sanctions on certain material imports from specific regions) and a mock company's supply chain data. It must identify affected products/suppliers, assess compliance risks, and generate a comprehensive risk report with mitigation recommendations.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Learning goals

What you should walk away with

  • Design complex, stateful graph-based agent workflows using LangGraph for regulatory analysis.

  • Implement hybrid instant/deep reasoning with Claude Opus 4.1 for nuanced risk assessment.

  • Develop dynamic LangGraph nodes that adapt to new regulatory information and risk profiles.

  • Integrate MCP for secure and governed access to diverse enterprise data systems.

Resources and assets

Reference links and supporting material

Dataset notes

Sample data for 1 tasks

Challenge setup

The solution will be evaluated on its ability to analyze a simulated new trade regulation (e.g., sanctions on certain material imports from specific regions) and a mock company's supply chain data. It must identify af...

Challenge input

{'new_regulation_text': 'string', 'company_profile': {'name': 'string', 'industry': 'string', 'suppliers': [{'id': 'string', 'country': 'string', '...

Agent execution

No agent tool is listed for this challenge.

Evaluated output

{'regulation_summary': 'string', 'impact_analysis': [{'product_id': 'string', 'risk_level': 'high|medium|low', 'reason': 'string', 'affected_suppli...

Evaluation checks
  • Verify the agent's summary of the new regulation is accurate and comprehensive.
  • Ensure all relevant products/suppliers affected by the regulation are correctly identified.
  • Confirm that the LangGraph agents successfully invoked simulated MCP-enabled tools for data access.
Configured evaluation evidence
  • CompletenessOfImpactAnalysis target: 90
Configured runtime
  • Python execution harness

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

No tool records are attached.

Evaluation contract

  • Verify the agent's summary of the new regulation is accurate and comprehensive.
  • Ensure all relevant products/suppliers affected by the regulation are correctly identified.
  • Confirm that the LangGraph agents successfully invoked simulated MCP-enabled tools for data access.
  • Verify agent logs demonstrate both instant screening and deeper analytical reasoning phases.

Metrics

  • CompletenessOfImpactAnalysis · Target 90
  • ReasoningDepthScore · Target 4

Workflow tasks

  1. Task 1

    Input: {'new_regulation_text': 'string', 'company_profile': {'name': 'string', 'industry': 'string', 'suppliers': [{'id': 'string', 'country': 'string', 'materials': ['string']}]}, 'products': [{'id': 'string', 'materials_used': ['string'], 'export_countries': ['string']}]}}

    Output: {'regulation_summary': 'string', 'impact_analysis': [{'product_id': 'string', 'risk_level': 'high|medium|low', 'reason': 'string', 'affected_suppliers': ['string']}]}

    Execution harness: Python

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Real-time Enterprise Compliance & Risk Assessment with Claude Opus 4.1, LangGraph, & MCP