Graph-Based Identity Security Agent

This challenge focuses on building an intelligent agent system capable of analyzing complex user, system, and access relationships to identify security risks. You will construct a knowledge graph representing an enterprise's identity landscape, then leverage Gemini 3 for advanced reasoning over this graph. Your primary task is to develop a graph-based agent workflow using LangGraph to traverse and query this knowledge graph. The agent will integrate with LlamaIndex for Retrieval-Augmented Generation (RAG) to fetch relevant context from the graph, and utilize MCP-enabled tools to interact with simulated enterprise identity systems (e.g., Active Directory, HR databases). The agent should be able to perform extended thinking with adaptive reasoning budgets to detect anomalous access patterns, privilege escalations, and provide risk assessments.

• Master LlamaIndex for building and querying a knowledge graph representing user, system, and access relationships within an enterprise.

• Implement LangGraph to define stateful, cyclic agent workflows that navigate and analyze the identity knowledge graph.

• Integrate Gemini 3 for sophisticated pattern recognition, contextual understanding, and risk assessment by providing it with retrieved graph data via RAG.

• Design MCP server-side implementations for simulated identity management systems (e.g., user directories, access control lists) and create agent tools to interact with them.

• Develop strategies for extended thinking within LangGraph, allowing the agent to dynamically allocate reasoning budget based on the complexity of the security query.

• Build a RAG pipeline using LlamaIndex to retrieve relevant nodes and edges from the knowledge graph based on agent queries, feeding this context to Gemini 3.