How to Create AI Agents: From Zero to Production (LangGraph, CrewAI, AutoGen)

This masterclass covers building autonomous AI agents from scratch using three modern Python frameworks: LangGraph, CrewAI, and AutoGen. The course progresses from foundational concepts through production-ready implementations, teaching how to design agent architectures, manage state, handle tool integration, and deploy agents at scale. Participants learn practical patterns for creating multi-agent systems, implementing memory management, and debugging complex agent workflows.

Key Points

• •Understand core agent architecture: perception (inputs) → reasoning (LLM decision-making) → action (tool execution) → feedback loops
• •LangGraph enables explicit state management and graph-based workflows for complex agent logic with built-in persistence
• •CrewAI provides high-level abstractions for multi-agent collaboration with role-based task assignment and hierarchical execution
• •AutoGen facilitates agent-to-agent communication patterns and conversation management for scalable multi-agent systems
• •Implement tool integration by defining schemas, handling errors gracefully, and validating outputs before agent consumption
• •Design memory systems: short-term (conversation context), long-term (vector stores), and episodic (execution history) for agent learning
• •Production deployment requires monitoring, logging, cost optimization, and graceful degradation when LLM APIs fail
• •Test agents systematically: unit test tools, integration test workflows, and evaluate agent decision quality against benchmarks
• •Handle edge cases: token limits, hallucinations, tool failures, and infinite loops through timeouts and validation gates
• •Choose framework based on use case: LangGraph for control, CrewAI for simplicity, AutoGen for complex multi-agent coordination

Workflow Diagram

Concepts