AI Agent Architecture: Reasoning, Memory, and LangGraph

Overview

"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"

Syllabus

• Foundations of Agentic AI Architecture
This module introduces the conceptual and structural foundations of agentic AI systems. Learners will explore how agents perceive their environment, make decisions, and act within defined workflows across a 4-hour learning experience.
• Building Structured and Reliable Agents
This 4-hour module introduces data consistency, structured schema validation, and logic control in AI agents through hands-on implementation using Pydantic-AI and LangGraph.
• Memory and Persistence in Agents
This 4-hour module explores the crucial role of memory in intelligent agents, focusing on persistence, recall, and performance optimization using Mem0.
• Building and Evaluating the Research Assistant Agent
This final 4-hour module focuses on system integration, testing, and reflection, where learners will build a functional research assistant agent and benchmark frameworks for practical use.

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Taught by

Board Infinity

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Subjects

Artificial Intelligence