{
  "id": "topic:agentic-rag",
  "_kind": "Topic",
  "_file": "domain/topics/topics-knowledge-patterns.yaml",
  "_cluster": "domain",
  "attributes": {
    "displayName": "Agentic RAG",
    "description": "Agentic RAG as a cross-cutting topic — agents that actively reason\nabout what to retrieve, iterate on retrieval results, and update\nknowledge rather than passively returning the top-k nearest vectors.\nCovers query planning (decompose complex questions into sub-queries),\nself-reflective retrieval (evaluate if retrieved context is sufficient,\nre-query if not), tool-augmented retrieval (agent uses calculator,\ncode execution, or API calls alongside vector search), corrective\nRAG (detect hallucination, re-retrieve), and the shift from\n\"retrieve then generate\" to \"reason, retrieve, reason, retrieve\"\nloops that characterize agentic knowledge work.\n"
  },
  "outgoingEdges": [
    {
      "from": "topic:agentic-rag",
      "to": "domain:software-engineering",
      "kind": "applies_to"
    },
    {
      "from": "topic:agentic-rag",
      "to": "specialization:ai-agents-conversational",
      "kind": "applies_to"
    }
  ],
  "incomingEdges": [
    {
      "from": "domain:knowledge-management",
      "to": "topic:agentic-rag",
      "kind": "contains"
    },
    {
      "from": "retrieval-pipeline:multi-hop-reasoning",
      "to": "topic:agentic-rag",
      "kind": "relates_to_topic"
    },
    {
      "from": "retrieval-pipeline:agentic-retrieval",
      "to": "topic:agentic-rag",
      "kind": "relates_to_topic"
    },
    {
      "from": "topic:rag-pipeline-design",
      "to": "topic:agentic-rag",
      "kind": "related_topics"
    },
    {
      "from": "topic:auto-discovery",
      "to": "topic:agentic-rag",
      "kind": "related_topics"
    }
  ]
}