Uploaded May 2026 | Updated September 2026, 2 weeks ago
🕵️ Your AI agent shouldn't need a decision tree to decide how to investigate.
The answers to complex questions often don’t live in a single data source. They span across multiple locations, domains, and vary in structure – tabular, connected, and unstructured, just to name a few.
Agentic GraphRAG gives your agent a toolkit and lets it choose which source(s) would be best:
🔹 A Neptune MCP server for open-ended knowledge graph exploration
🔹 Custom domain tools for defined, repeatable, and high-confidence subgraph extraction
🔹 Auto-generated domain tools to query over unstructured documents
We demo a fraud detection use case with #AmazonNeptune & #AmazonBedrock, but the architecture works for any domain where your #agenticAI needs to follow leads across structured & unstructured data.
Stop orchestrating & start letting your agent investigate.
🕵️ Your AI agent shouldn't need a decision tree to decide how to investigate.
The answers to complex questions often don’t live in a single data source. They span across multiple locations, domains, and vary in structure – tabular, connected, and unstructured, just to name a few.
Agentic GraphRAG gives your agent a toolkit and lets it choose which source(s) would be best:
🔹 A Neptune MCP server for open-ended knowledge graph exploration
🔹 Custom domain tools for defined, repeatable, and high-confidence subgraph extraction
🔹 Auto-generated domain tools to query over unstructured documents
We demo a fraud detection use case with #AmazonNeptune & #AmazonBedrock, but the architecture works for any domain where your #agenticAI needs to follow leads across structured & unstructured data.
Stop orchestrating & start letting your agent investigate.
