{
  "slug": "agentic-rag",
  "title": "Agentic RAG: Retrieval as a Tool for AI Agents",
  "description": "What agentic RAG is and how it differs from single-shot retrieval: tool contracts, iterative-retrieval budgets, multi-hop decomposition, and the trust boundary moving into the retrieval path.",
  "category": "Guide",
  "tags": [
    "rag",
    "agentic-rag",
    "retrieval",
    "tool-calling",
    "agents",
    "multi-hop"
  ],
  "updated": "2026-09-04",
  "premium": false,
  "rights": {
    "access": "free",
    "license": "https://changegamer.ai/license.xml",
    "pricing": "https://changegamer.ai/api/pricing.json",
    "payment": "https://changegamer.ai/api/payment.json"
  },
  "canonical": "https://changegamer.ai/resources/agentic-rag",
  "markdown": "https://changegamer.ai/resources/agentic-rag.md",
  "outline": [
    {
      "depth": 2,
      "text": "Key facts",
      "anchor": "key-facts"
    },
    {
      "depth": 2,
      "text": "Tool contract, budgets, and trust",
      "anchor": "tool-contract-budgets-and-trust"
    }
  ],
  "related": [
    {
      "slug": "choosing-a-vector-database",
      "title": "Choosing a Vector Database",
      "description": "Criteria-based decision guide: dedicated vs. add-on vector stores, scale thresholds, hybrid search support, self-host vs. managed, and a start-here recommendation.",
      "url": "https://changegamer.ai/resources/choosing-a-vector-database"
    },
    {
      "slug": "chunking-strategies-for-rag",
      "title": "Chunking Strategies for RAG",
      "description": "Practitioner reference for chunking documents before embedding: fixed-size, recursive, semantic, late chunking, and contextual retrieval — with a strategy comparison table, chunk-size and overlap tradeoffs, code/table/Markdown handling, embedding model context limits, and evaluation methods.",
      "url": "https://changegamer.ai/resources/chunking-strategies-for-rag"
    },
    {
      "slug": "hybrid-search-for-rag",
      "title": "Hybrid Search for RAG: BM25 + Dense Retrieval and Fusion",
      "description": "How to combine lexical (BM25/SPLADE) and dense vector retrieval with Reciprocal Rank Fusion for higher first-stage recall in RAG pipelines — with the RRF formula, a sparse-method comparison table, and verified DB support.",
      "url": "https://changegamer.ai/resources/hybrid-search-for-rag"
    },
    {
      "slug": "graphrag-for-agents",
      "title": "Knowledge Graphs and GraphRAG for Agents",
      "description": "Graph-structured retrieval: when and how to use knowledge graphs over vector RAG for multi-hop, relational, and global corpus queries.",
      "url": "https://changegamer.ai/resources/graphrag-for-agents"
    }
  ],
  "furtherReading": [
    {
      "slug": "agentic-retrieval-patterns",
      "title": "Retrieval as a Tool: Agentic RAG Patterns That Survive Production",
      "description": "When AI agents consume retrieval as a tool rather than a pipeline stage: narrow tool contracts, hard budgets on iterative retrieval, query decomposition, provenance-carrying results, and keeping trust boundaries inside the retrieval path.",
      "url": "https://changegamer.ai/articles/agentic-retrieval-patterns"
    },
    {
      "slug": "graphrag-vs-vector-rag",
      "title": "GraphRAG vs Vector RAG: When to Use a Knowledge Graph Instead",
      "description": "A decision framework for choosing graph-structured retrieval over standard vector RAG: which query types GraphRAG actually wins, what building a knowledge graph costs, named implementations, and hybrid vector-plus-graph patterns.",
      "url": "https://changegamer.ai/articles/graphrag-vs-vector-rag"
    }
  ],
  "body": "Agentic RAG is retrieval-augmented generation where the model, not a fixed pipeline, decides whether to retrieve, writes its own queries, and iterates on what comes back — unlike single-shot RAG, which always retrieves once regardless of need. The retrieval engineering underneath doesn't change; what changes is who drives it.\n\n## Key facts\n\n- Agentic RAG hands retrieval decisions to the model itself: it decides whether to retrieve, writes its own queries, reads results, and issues follow-ups, whereas single-shot RAG always retrieves once per query regardless of need.\n- The standard integration pattern exposes retrieval as a single callable MCP tool — embed query, ANN search, rerank, return chunks — that the agent calls explicitly when it needs grounded context, enabling conditional and multi-hop retrieval.\n- Multi-hop questions that depend on an intermediate entity (\"who founded the company that acquired X?\") fail as one dense query but succeed as a decomposed sequence where each hop's result informs the next.\n- Iterative retrieval needs deterministic budgets — a cap on retrieval rounds, a token ceiling, and explicit stop conditions — since an unbounded \"retrieve until satisfied\" loop turns into an unbounded bill.\n- Retrieved text becomes an observation that can trigger further tool calls, so agentic systems must treat it as untrusted input: strip instruction-like framing where feasible and carry chunk-level attribution through every step.\n\n## Tool contract, budgets, and trust\n\nA retrieval tool exposed to an agent needs the same rigor as any tool definition: explicit parameters (query text, filters, result size), a result shape carrying slug, title, snippet, score, and stable URLs, and a description written for a literal-minded reader — the agent's only documentation. Give every task a budget of three numbers (rounds, tokens, stop conditions) and log agent-issued queries separately from human ones, since their failure modes differ.\n\nThe trust boundary moves too: in single-shot RAG, injected content in a chunk influences one answer; in agentic RAG the same text becomes an observation that can drive further tool calls and spend. Stripping instruction-like content, attributing every fact to its source chunk, and scoping which downstream tools an answering process may invoke move from optional hardening to a launch requirement.\n\nFor the full retrieval pipeline this sits on top of — chunking, hybrid search, reranking, evaluation — see /resources/rag-retrieval-for-agents. For tool-contract design and reliability patterns, see /resources/reliable-tool-calling. For the broader memory and context picture, see /resources/agent-memory-context.",
  "sources": []
}