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AI Agent Architecture: Reasoning, Memory, and LangGraph

AI Agent Architecture: Reasoning, Memory, and LangGraph

This course is part of Building and Deploying AI Agents with LLMs and LangChain Specialization

Instructor: Board Infinity

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Design modular AI agent architectures using LangGraph for perception, reasoning, and action workflows
Implement structured input/output validation in agents using Pydantic-AI schemas
Integrate Mem0 for short-term, long-term, and contextual memory in agent systems

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Build your subject-matter expertise

This course is part of the Building and Deploying AI Agents with LLMs and LangChain Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

"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"

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.

What's included

10 videos4 readings5 assignments

10 videosTotal 55 minutes

Career Opportunities in AI Agent Architecture6 minutes
Industry Trends: From Chatbots to Reasoning Agents6 minutes
Skills and Tools in Demand7 minutes
The Perception-Reasoning-Action Model6 minutes
Mapping Lifecycle Stages to Real-World Tasks5 minutes
Interaction Loops and Feedback in Agents5 minutes
Comparing Architectural Paradigms4 minutes
Benefits of Modular Design6 minutes
Introduction to Graph-Based Reasoning4 minutes
Building a Simple LangGraph Workflow6 minutes

4 readingsTotal 90 minutes

Career Scope in Agentic AI Systems15 minutes
Understanding the Agent Lifecycle15 minutes
Modular vs. Monolithic Architectures30 minutes
Graph-Based Reasoning with LangGraph30 minutes

5 assignmentsTotal 180 minutes

Foundations of Agentic AI Architecture60 minutes
Career Scope in Agentic AI Systems30 minutes
Understanding the Agent Lifecycle30 minutes
Modular vs. Monolithic Architectures30 minutes
Graph-Based Reasoning with LangGraph30 minutes

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.

What's included

7 videos3 readings4 assignments

7 videosTotal 33 minutes

Why Structure Matters in Agent Systems4 minutes
Enforcing Validation at Runtime7 minutes
Designing Reasoning Nodes5 minutes
Integrating I/O with LangGraph4 minutes
Debugging Workflow Dependencies5 minutes
Building Extraction Nodes4 minutes
Classification and Storage Nodes4 minutes

3 readingsTotal 45 minutes

Structured Data in Agents15 minutes
Implementing LangGraph Workflows15 minutes
Hands-On: Building the Email-to-Task Agent15 minutes

4 assignmentsTotal 150 minutes

Building Structured and Reliable Agents60 minutes
Structured Data in Agents30 minutes
Implementing LangGraph Workflows30 minutes
Hands-On: Building the Email-to-Task Agent30 minutes

This 4-hour module explores the crucial role of memory in intelligent agents, focusing on persistence, recall, and performance optimization using Mem0.

What's included

4 videos3 readings4 assignments

4 videosTotal 20 minutes

Memory Types and Functions5 minutes
Real-World Applications of Memory in Agents5 minutes
Managing Memory States4 minutes
Performance vs. Context Trade-offs6 minutes

3 readingsTotal 45 minutes

Understanding Memory Models15 minutes
Implementing Persistent Memory with Mem015 minutes
Evaluating Memory Performance15 minutes

4 assignmentsTotal 150 minutes

Memory and Persistence in Agents60 minutes
Understanding Memory Models30 minutes
Implementing Persistent Memory with Mem030 minutes
Evaluating Memory Performance30 minutes

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.

What's included

7 videos3 readings4 assignments

7 videosTotal 32 minutes

Combining Components (LangGraph + Mem0 + Pydantic-AI)5 minutes
Testing Structured Output3 minutes
Agent Design and Architecture5 minutes
Implementing Summarization and Recall4 minutes
Testing with Research Documents4 minutes
Comparing LangGraph, LangChain, and Agno5 minutes
Selecting the Right Framework5 minutes

3 readingsTotal 45 minutes

System Integration and Testing15 minutes
Building the Research Assistant Agent15 minutes
Framework Evaluation and Reflection15 minutes

4 assignmentsTotal 150 minutes

Building and Evaluating the Research Assistant Agent60 minutes
System Integration and Testing30 minutes
Building the Research Assistant Agent30 minutes
Framework Evaluation and Reflection30 minutes

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Instructor

Board Infinity

252 Courses410,592 learners

Offered by

Board Infinity

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Frequently asked questions

No prior agent-building experience is required. Basic Python and some familiarity with LLMs will help you get the most out of the hands-on labs.

You'll work with LangGraph for workflow orchestration, Pydantic-AI for structured data validation, and Mem0 for persistent memory. LangChain and Agno are covered for comparison.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.