{
  "id": 11248336,
  "title": "GraphProbe AI: Building an Agentic GraphRAG System with TigerGraph",
  "url": "https://urgent.news/2026/10/01/graphprobe-ai-building-an-agentic-graphrag-system-with-tigergraph",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-01T17:55:56.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/sreevalli_335df1977399966/graphprobe-ai-building-an-agentic-graphrag-system-with-tigergraph-an2"
  },
  "original_language": "en",
  "account": "GraphProbe AI is an innovative system designed to tackle complex questions that require more than simple text retrieval. It employs an \"Investigate. Connect. Verify.\" approach to handle questions that involve multiple entities, relationships, and evidence evaluation. Conventional retrieval pipelines typically follow a linear pattern of question → retrieve documents → generate answer. However, these systems struggle with questions that demand deeper analysis, such as identifying entities connected through multiple relationships or determining what additional information is needed to verify an answer. GraphProbe AI addresses these challenges by integrating three retrieval strategies: RAG for retrieving textual information, GraphRAG for providing contextual graph-based information, and Agentic GraphRAG which orchestrates the overall investigation process. The system dynamically adapts its approach based on the discovered entities, evidence gaps, and remaining information needs. At the heart of GraphProbe AI is an orchestrator that guides a series of investigation steps, which may include entity linking, graph traversal, similarity search, evidence aggregation, multi-hop reasoning, evidence evaluation, and verification. This adaptive workflow allows the system to choose the most appropriate strategy for each unique question. TigerGraph serves as the foundation for GraphProbe AI, providing a robust graph database to accurately represent entities and their connections. By leveraging TigerGraph's capabilities, the system can effectively answer questions that require understanding relationships between entities and utilizing multi-hop reasoning. GraphProbe AI not only generates answers but also tracks the evidence used throughout the investigation process. It records information such as retrieved evidence, connected entities, graph traversal results, retrieval operations, evidence evaluation, verification steps, agentic trace, tokens used, investigation latency, and stopping reason. This comprehensive investigation trace, or \"agentic trace,\" provides a detailed account of the reasoning process, enabling users to inspect the system's decision-making and evaluate the support for the generated answer. The system also includes a comparison between different retrieval strategies, such as traditional RAG, GraphRAG, and the more adaptive Agentic GraphRAG approach. By examining retrieval behavior, evidence usage, token consumption, latency, and investigation traces, the system can provide insights into the strengths and weaknesses of each approach. The GraphProbe AI application presents an investigation workspace where users can submit questions and view the generated answer, connected entities, supporting evidence, and the detailed agentic investigation trace. It also offers views for comparing retrieval approaches and examining benchmark metrics, allowing users to assess the system's performance. During evaluation, GraphProbe AI was tested against provided evaluation questions, and its outputs included raw answers along with token usage information.",
  "summary": "GraphProbe AI: Building an Agentic GraphRAG System with TigerGraph Modern RAG systems are good at finding relevant pieces of information. But when a question requires connecting multiple entities, following relationships, checking evidence, and deciding what information is still missing, simple retrieval can fall short. That is the problem we explored with GraphProbe AI . Investigate. Connect.…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}