Do I need prior experience with AI agents to take this course?

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.

What tools and frameworks will I use in this course?

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.

When will I have access to the lectures and assignments?

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.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

AI Agent Architecture: Reasoning, Memory, and LangGraph

Ends today! Save on skills that make you shine with 40% off 3 months of Coursera Plus. Save now

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

Learn more

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

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

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"

Module details

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 Architecture5 minutes
Industry Trends: From Chatbots to Reasoning Agents7 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 Paradigms5 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

Career Scope in Agentic AI Systems30 minutes
Understanding the Agent Lifecycle30 minutes
Modular vs. Monolithic Architectures30 minutes
Graph-Based Reasoning with LangGraph30 minutes
Foundations of Agentic AI Architecture60 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 32 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

Structured Data in Agents30 minutes
Implementing LangGraph Workflows30 minutes
Hands-On: Building the Email-to-Task Agent30 minutes
Building Structured and Reliable Agents60 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 19 minutes

Memory Types and Functions5 minutes
Real-World Applications of Memory in Agents5 minutes
Managing Memory States4 minutes
Performance vs. Context Trade-offs5 minutes

3 readingsTotal 45 minutes

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

4 assignmentsTotal 150 minutes

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