testing

Execute and Evaluate Risk Assessment

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: Graph-Based Identity Security Agent

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Linked challenge

Graph-Based Identity Security Agent

Prompt source

Original prompt text with formatting preserved for inspection.

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Run your LangGraph agent with Gemini 2.5 Pro against the sample knowledge graph with various security queries. Capture the 'identified_risks' and 'reasoning_path' as JSON output. Analyze how the adaptive thinking budget influenced the agent's decision-making and graph traversal efficiency. Propose improvements based on the output.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Preserve the rubric, target behavior, and pass-fail criteria as the baseline for evaluation.

Tune next

Adjust fixtures, mocks, and thresholds to the system under test instead of weakening the assertions.

Verify after

Make sure the prompt catches regressions instead of just mirroring the happy-path examples.

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Purpose

testing

This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked challenge

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.

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