Agentic RAG Explained: Multi-Agent, Production Patterns and ReAct- When AI Decides How to Search

Описание: Same question. Traditional RAG searches once and gives a vague answer. Agentic RAG breaks the question down, searches multiple times, self-corrects, and nails it.

This is the future of RAG — AI agents that THINK before they search, verify what they found, and loop back when they need more. In this deep dive, I cover everything from the core patterns to real production architectures.

⏱️ TIMESTAMPS:
0:00 — Side-by-side: Traditional RAG vs Agentic RAG
2:00 — What is Agentic RAG? (4 core patterns)
5:00 — The ReAct Pattern: How agents think
8:00 — Architecture deep dive (single-agent, router, multi-agent)
13:00 — Frameworks: LangGraph, LlamaIndex, CrewAI, AutoGen, AWS Bedrock
17:00 — Real-world use cases (Enterprise, Finance, Support, Legal)
21:00 — When NOT to use Agentic RAG
23:00 — Challenges & pitfalls (loops, cost, hallucinations)
26:00 — Building your first Agentic RAG (7-step roadmap)
30:00 — Pipeline RAG vs Agentic RAG vs GraphRAG
33:00 — Key takeaways & series recap

🧠 CORE PATTERNS COVERED:
— Planning (query decomposition)
— Tool Use (multi-tool, multi-source)
— Reflection (self-correction, CRAG)
— Multi-Agent Collaboration (supervisor + specialists)

🏗️ ARCHITECTURES:
— Single-Agent ReAct Loop
— Router Pattern (adaptive complexity)
— Multi-Agent with Supervisor
— Evidence Gap Analysis
— Pipeline vs Agentic vs GraphRAG comparison

🛠️ FRAMEWORKS:
— LangGraph, LlamaIndex Agents, CrewAI, AutoGen
— AWS Bedrock Agents, Azure AI Agent Service
📚 THIS IS PART 5 OF MY RAG SERIES:

RAG Explained →    • RAG in 10 Minutes: Retrieval-Augmented Gen...  
Chunking Strategies →    • 10 Chunking Strategies for RAG - Every Met...  
Advanced RAG (Reranking, Hybrid Search) →    • Why Your RAG System Retrieves Garbage - Ho...  
Observability Deep Dive →    • OpenTelemetry is Changing Everything — Obs...  
Agentic RAG (this video)

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