Agentic AI Engineering: Building Multi-Agent Systems with LangGraph, CrewAI & AutoGen

Introduction to Agentic AI

• What is Agentic AI?

• Agents vs traditional LLM applications

• Planning, reasoning, and acting loop

• Tool usage and function calling

• Memory (short-term vs long-term)

• Single-agent vs multi-agent architectures

Core Agent Design Patterns

• ReAct framework

• Reflection and critique loops

• Planner-executor pattern

• Tool selection strategies

• Error handling and retry logic

LangGraph Fundamentals

• What is LangGraph?

• Graph-based orchestration model

• Nodes, edges, and state management

• Conditional branching

• Persistent state handling

Building Stateful Agents with LangGraph

• Defining agent nodes

• Tool integration

• Managing execution flow

• Debugging agent graphs

• Observability strategies

Hands-on Labs

• Build a basic tool-calling agent

• Create graph-based workflow

• Implement conditional transitions

• Add memory store

• Debug execution flow

Introduction to Microsoft AutoGen

• Conversational agent architecture

• Agent-to-agent communication

• Tool and code execution integration

• Human-in-the-loop systems

Designing Autonomous Agent Conversations

• Chat-based agent workflows

• Planner-agent collaboration

• Code interpreter agent

• Feedback loops

• Safety guardrails

Enterprise Architecture for Agentic Systems

• Deployment patterns (API-based)

• Logging and observability

• Rate limiting and concurrency control

• Security considerations

• Data privacy and governance

Evaluation & Monitoring of Agent Systems

• Agent performance metrics

• Task success rate measurement

• Hallucination detection

• Prompt injection mitigation

• Continuous improvement strategy

Capstone Project: End-to-End Agentic Workflow

• Design multi-agent architecture

• Integrate external APIs and tools

• Implement LangGraph + CrewAI or AutoGen hybrid

• Add monitoring and error handling

• Present design trade-offs and scalability plan

Upon completion, participants will be able to:

• Design and implement single-agent and multi-agent systems

• Build graph-based orchestrated workflows using LangGraph

• Develop collaborative agent teams with CrewAI

• Implement conversational multi-agent systems using AutoGen

• Integrate external tools and APIs into agent workflows