Module 8: AI Agents & Agentic RAG Slides

Slide Outline

1. AI Agents & Agentic RAG - Tool calling, memory systems, multi-agent architectures, and LangGraph orchestration
2. Learning Objectives - 4 outcomes for this module
3. Why This Module Matters - Basic RAG answers simple questions. Agentic RAG handles complex, multi-step questions that require reasoning, multiple d
4. From Pipeline to Agent - Lesson section from the full module
5. Tool Calling - Lesson section from the full module
6. Multi-Agent Architectures - Lesson section from the full module
7. Memory Systems - Lesson section from the full module
8. Common Mistakes to Avoid - 4 mistakes covered
9. Hands-On Labs - 2 hands-on labs
10. Key Takeaways - 5 points to remember

Learning Objectives

• Build AI agents that reason and use tools
• Implement agentic RAG with dynamic retrieval
• Design multi-agent systems for complex tasks
• Use LangGraph for agent orchestration

Why This Module Matters

Basic RAG answers simple questions. Agentic RAG handles complex, multi-step questions that require reasoning, multiple data sources, and tool use. This is where AI applications become genuinely useful for enterprise workflows.

Common Mistakes

• Building agents without reliable basic RAG first
• Not limiting agent tool calls (runaway agents are expensive)
• Not implementing guardrails for tool access (agent with SQL access can drop tables)
• Using agents for simple questions (overkill adds latency and cost)

Key Takeaways

• Agentic RAG decides WHAT to retrieve dynamically — not a fixed pipeline
• Tool calling gives agents access to databases, search, APIs, and calculators
• Multi-agent systems split complex tasks across specialized agents
• LangGraph orchestrates agent workflows with state management
• Memory systems enable context-aware multi-turn interactions

Hands-On Labs

1. Build a Multi-Tool Agent

   Create an AI agent that reasons about tool selection.

   40 min - Advanced

   • Define tools: search, database, calculator
   • Implement the agent loop (reason → act → observe)
   • Test with queries requiring different tools
   • Handle multi-step queries requiring multiple tools

   View lab files on GitHub

2. Multi-Agent Orchestration with LangGraph

   Build a multi-agent system for complex tasks.

   45 min - Advanced

   • Design agent roles: researcher, analyst, writer
   • Build a LangGraph workflow connecting agents
   • Test with complex questions requiring collaboration
   • Add memory for conversation continuity

   View lab files on GitHub