AI Agent Architecture: Reasoning, Memory, and LangGraph

О курсе

"Architecting AI Agents for Real-World Systems is a hands-on course designed for developers, AI engineers, and technical professionals who want to build production-grade agentic AI systems using LangGraph, Mem0, and Pydantic-AI. You'll learn how to design modular agent architectures, implement structured I/O, add persistent memory, and evaluate frameworks for real deployment. Module 1 introduces the foundations of agentic AI, covering the perception–reasoning–action lifecycle, modular vs. monolithic design, and graph-based reasoning with LangGraph. Module 2 focuses on building structured and reliable agents, using Pydantic-AI for schema validation and LangGraph for workflow orchestration, culminating in an Email-to-Task agent. Module 3 explores memory and persistence, where you'll implement Mem0 to give your agents short-term, long-term, and contextual memory, then benchmark recall and performance. Module 4 integrates all components into a functional Research Assistant Agent and compares LangGraph, LangChain, and Agno for production readiness. By the end of this course, you will: - Design modular agent workflows using LangGraph nodes and edges - Implement structured I/O validation with Pydantic-AI - Add persistent memory to agents using Mem0 - Evaluate and select the right agentic framework for real-world deployment"

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