Unlock the future of AI-driven insights by combining Retrieval-Augmented Generation (RAG) with AI agents and LangGraph’s graph-based orchestration. This episode breaks down how multi-step reasoning loops and precise workflow control transform AI from a simple Q&A tool into a dynamic problem solver — a must-know for product leaders, founders, and decision-makers.

In this episode:

- How AI agents add reasoning loops to RAG for self-correcting, multi-step problem solving

- What makes LangGraph’s graph approach unique in managing AI workflows with memory and control

- Why this combination reduces AI hallucinations and boosts answer accuracy

- Practical trade-offs between traditional RAG, agent frameworks like LangChain, and LangGraph

- Real-world use cases in customer support, compliance, and enterprise knowledge management

- Key challenges and future directions for scalable, reliable agent-enhanced RAG systems

Key tools & technologies mentioned:

- Retrieval-Augmented Generation (RAG)

- AI Agents

- LangGraph

- LangChain

- Large Language Models (LLMs)

- External tool integrations (e.g., TavilySearch, Retriever Tool)

Timestamps:

00:00 – Introduction & Guest Welcome

02:30 – The Power of Adding Reasoning Loops with AI Agents

06:00 – LangGraph’s Graph-Based Workflow Orchestration Explained

10:00 – Comparing Traditional RAG, AgentExecutor, ReAct, and LangGraph

13:30 – Under the Hood: AgentState, Conditional Edges & Tool Integration

16:30 – Business Impact & Real-World Applications

19:00 – Challenges, Risks, and Strategic Considerations

20:30 – Closing Thoughts & Book Spotlight

Resources:

- "Unlocking Data with Generative AI and RAG" by Keith Bourne - Search for 'Keith Bourne' on Amazon and grab the 2nd edition

- Explore Memriq AI for AI tools and resources: https://Memriq.ai