Agentic RAG

Agentic RAG - Self-Correcting Retrieval

Overview

Agentic RAG evolves beyond static information retrieval by embedding autonomous agents within the retrieval and generation pipeline. Unlike "Naive RAG" which assumes a direct mapping from query to document chunk, Agentic RAG employs iterative reasoning, self-correction, and multi-step workflows to ensure answers are grounded, accurate, and comprehensive. It treats retrieval as a dynamic task-oriented process.

When To Use

• When dealing with multi-hop questions requiring information synthesis from disparate sources.
• When existing RAG pipelines suffer from high hallucination rates or low retrieval precision.
• When the domain requires "Codebase RAG" that understands syntax, imports, and symbol definitions rather than just text semantic similarity.
• When you need systems that can autonomously fall back to web search or tool execution when internal knowledge is insufficient.

Architecture Patterns

1. Query Decomposition & Routing: Breaking down complex, high-level questions into focused sub-queries. Agents route these sub-queries to appropriate specialized indexes (e.g., code-index, docs-index, general-web).
2. Hybrid Retrieval + RRF: Combining lexical search (BM25 for acronyms/technical IDs) with dense embedding search (vector similarity), merged using Reciprocal Rank Fusion (RRF) to boost ranking robustness.
3. Corrective RAG (CRAG): Implementing a relevance grader that evaluates retrieved docs. If quality is low, the agent triggers a fallback workflow (e.g., web search, re-phrasing).
4. Self-RAG Reflection Loops: Generation output is passed through an evaluator agent that checks for groundedness and relevance. If it fails, the system triggers a re-retrieval or re-generation cycle.
5. Codebase RAG (AST-aware): Rather than naive chunking, use AST (Abstract Syntax Tree) parsing to extract class/function definitions and method signatures, ensuring the retriever captures the structural context of the codebase.

Implementation Workflow

1. Data Ingestion:
   • Parse documents with layout-aware tools.
   • For code: extract symbols, classes, and dependencies using tree-sitter.
   • Generate embeddings using multi-modal or code-specialized models (e.g., text-embedding-3-large).
2. Retrieval:
   • Apply hybrid search (BM25 + Vectors).
   • Use cross-encoder rerankers (e.g., Cohere Rerank, BGE-Reranker) to refine the top-k results.
3. Agentic Processing:
   • Decomposition Phase: Use LLM to split user query into atomic tasks.
   • Retrieval Phase: Fetch data for each task independently.
   • Grading Phase: Use a "Critic" agent to grade relevance/faithfulness.
   • Generation Phase: Synthesize the answer.
4. Validation:
   • Hallucination Detection: Compare generation against original retrieved contexts using NLI (Natural Language Inference) models or LLM-as-a-judge.

Quality Gates

• Grounding Gate: Reject any answer where the supporting evidence score is below a predefined threshold (e.g., 0.8 on a 0-1 scale).
• Retrieval Quality Gate: If all retrieved segments have low relevance scores, block generation and trigger an automated refinement or search process.
• Syntactic Integrity Gate (Code RAG): Verify that retrieved code snippets can be resolved/linked back to real codebase identifiers.
• Confidence Scoring: Require agents to output a confidence score; if low, provide a disclaimer or suggest human intervention.

References

• CRAG (Corrective RAG)
• Self-RAG: Learning to Retrieve, Generate, and Critique
• Reciprocal Rank Fusion (RRF)
• Agentic Retrieval-Augmented Generation

[!NOTE] Agentic RAG introduces latency overhead. Always measure RTT (Round Trip Time) during the evaluation phase to ensure acceptable UX.

[!TIP] For codebase RAG, prefer tool-based indexing (e.g., repomix) over raw file chunking to preserve module boundaries.

[!WARNING] Ensure PI-masking is performed before indexing private repositories, especially when using third-party embedding providers.