implementation

Data Ingestion and Knowledge Graph Construction

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Linked challenge: AI-Powered Critical Mineral Supply Chain Security Assessment

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Linked challenge

AI-Powered Critical Mineral Supply Chain Security Assessment

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Describe your approach to ingesting diverse data sources (e.g., news APIs, trade databases, government publications) and transforming them into a structured format suitable for the AI agents. Consider using a knowledge graph or a structured data model to represent relationships between companies, assets, regions, and geopolitical events. How will this structured data be made accessible to the LangGraph agents?

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implementation

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Linked challenge

AI-Powered Critical Mineral Supply Chain Security Assessment

Critical infrastructure relies heavily on secure supply chains for essential resources like critical minerals. Geopolitical shifts, mergers, and acquisitions can introduce significant vulnerabilities. This challenge requires you to design and implement an advanced AI agent system that proactively assesses the security implications of such corporate actions on critical mineral supply chains. The system should identify potential points of failure, geopolitical risks, and regulatory non-compliance, providing actionable insights for mitigation. Leveraging a powerful Large Language Model like Llama 3.3 (70B) orchestrated through LangGraph, you will build dynamic, multi-step reasoning capabilities. The solution should be deployed on Google Vertex AI for scalable and managed execution, integrating real-time geopolitical data, trade statistics, and regulatory frameworks to simulate expert analysis of complex, evolving scenarios.

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