Overview
Agentic RAG transcends traditional RAG by embedding autonomous AI agents that dynamically manage retrieval strategies and iteratively refine contextual understanding.
Traditional vs. Agentic RAG
| Aspect | Traditional RAG | Agentic RAG |
|---|---|---|
| Retrieval | Single-shot | Iterative, adaptive |
| Query | Fixed | Dynamically refined |
| Sources | Predetermined | Discovered on-demand |
| Reasoning | Linear | Multi-hop |
| Evaluation | None | Continuous |
Agentic RAG Components
Query Planning Agent
- Decomposes complex questions
- Identifies information needs
- Plans retrieval sequence
Retrieval Agent
- Executes searches across sources
- Handles multiple retrieval methods
- Manages API integrations
Evaluation Agent
- Assesses document relevance
- Identifies gaps
- Triggers additional retrieval
Synthesis Agent
- Combines retrieved information
- Resolves contradictions
- Generates coherent responses
Architectural Patterns
Single-Agent Agentic RAG
One agent handles query refinement and retrieval decisions.
Multi-Agent Agentic RAG (MA-RAG)
Specialized agents for each pipeline stage:
- Planner → Definer → Extractor → QA agents
Hierarchical Agentic RAG
Layers of agents with supervisors delegating to specialists.
Corrective RAG Workflow
Query → Retrieve → Evaluate Relevance
↓
Relevant? → No → Refine Query → Retrieve Again
↓
Yes
↓
Sufficient? → No → External Search → Retrieve More
↓
Yes
↓
Synthesize
Research Results
"Even a small LLaMA3-8B model equipped with MA-RAG surpasses larger standalone LLMs" on complex QA tasks, demonstrating that multi-agent collaboration compensates for individual model limitations.
Domain Applications
- Healthcare: Multi-hop reasoning for clinical decisions
- Finance: Acronym resolution, regulatory search
- Legal: Contract analysis, case law retrieval
- Research: Literature review, fact synthesis