AI Agent Architecture: Reasoning, Memory, and LangGraph | Board Infinity
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Course 1 of 3 Building and Deploying AI Agents Specialization

AI Agent Architecture: Reasoning, Memory, and LangGraph

Learn how real AI agents are built - design modular reasoning workflows in LangGraph, enforce structured inputs and outputs with Pydantic-AI, and give agents memory with Mem0 - then bring it all together in a Research Assistant Agent.

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≈ 17 hours 4 modules Intermediate level Taught in English
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Board Infinity Board InfinityInstructor · 287 courses on Coursera
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17 assignments and two hands-on agent builds

Certificate included

Shareable on LinkedIn the day you finish

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Board Infinity: 287 courses, 480,000+ learners on Coursera

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01 About the course

Architecting AI agents for real-world systems.

Four modules: agent foundations, structured and reliable agents, memory and persistence, and a capstone Research Assistant Agent.

4
Modules
Foundations to capstone
28
Video lessons
Short, hands-on
17
Assignments
Practice and graded quizzes
17h
Total learning time
Self-paced, flexible deadlines

AI Agent Architecture: Reasoning, Memory, and LangGraph goes beyond prompting a chatbot. It covers how AI agents perceive, reason and act, and how to architect them so they're modular, reliable and able to remember - the difference between a demo and a system you can run.

By the end, you can design modular agent architectures with LangGraph, validate structured inputs and outputs with Pydantic-AI, and integrate Mem0 for short-term, long-term and contextual memory - across 28 short video lessons, 13 readings and 17 assignments.

It's course 1 of Board Infinity's Building and Deploying AI Agents with LLMs and LangChain specialization on Coursera - the architecture foundation for multi-agent systems and production deployment.

Inside the course
01Foundations of agentic AI architecture≈ 5h 02Building structured and reliable agents≈ 4h 03Memory and persistence in agents≈ 4h 04Building the Research Assistant Agent≈ 4h
02 Who it's for

Built for developers moving from LLM demos to real agents.

If you can write some Python and have used LLMs, the course was written with you in mind.

Developers

Python developers

Add agent architecture, LangGraph and memory to the code you already write.

AI / ML

AI & ML engineers

Turn models into agents that reason, validate their output and remember context.

Engineers

Software engineers moving into AI

Apply the modular design you know to agentic systems.

Architects

Tech leads & architects

Choose between LangGraph, LangChain and Agno with clear criteria.

03 What you'll learn

Four outcomes. Agents that actually work.

Each module adds a layer to your agent, from the architecture to a full Research Assistant you test on real documents.

Module 1

Design modular agent architectures

Map the perception-reasoning-action model and agent lifecycle, compare modular and monolithic designs, and build a LangGraph workflow.

Module 2

Build structured, reliable agents

Enforce runtime validation with Pydantic-AI, design reasoning nodes, and build an Email-to-Task agent.

Module 3

Give agents memory

Use short-term, long-term and contextual memory with Mem0, and balance performance against context.

Module 4

Build a Research Assistant Agent

Integrate everything into an agent that summarizes and recalls research, then compare LangGraph, LangChain and Agno.

1:1 coaching · optional add-on

Build with 1:1 coaching from industry experts.

A dedicated coach from your target domain: someone who has done the job, hired for it, and knows exactly what it takes.

Book a demo session
  • A coach matched to your goal in 24 hours
  • A written roadmap you keep
  • Weekly sessions for as long as you want them

60-minute demo session · 100% refundable · no obligation to continue

Review your agent design

Nodes, state and memory, checked by someone who builds agents for a living.

Debug your workflows

Work through validation failures and graph dependencies that stall agents.

Plan a portfolio agent

An agent project that shows employers you can architect, not just prompt.

Aim it at a role

Which AI engineering roles this opens, and what to learn next.

2,000+Industry coaches
16Domains covered
4.8/5Average coach rating
48,000+Sessions delivered
04 Syllabus

Four modules, from agent foundations to a working research assistant.

About 17 hours of learning - Coursera suggests 2 weeks at 10 hours a week, fully self-paced. Every module lists its learning objectives, its content, and what you'll walk out with.

28 videos 13 readings 17 assignments ≈ 17 hours Download full syllabus
Learning objectives - by the end of this module you can:
  • Describe career opportunities, industry trends and in-demand skills in agentic AI
  • Map the perception-reasoning-action model and agent lifecycle to real-world tasks
  • Compare modular and monolithic agent architectures
  • Build a simple graph-based reasoning workflow in LangGraph
What's inside
10 videos55 min
4 readings90 min
5 assignments180 min
Topics covered
Careers in agent architecture Chatbots to reasoning agents Perception-reasoning-action Agent lifecycle Interaction & feedback loops Modular vs. monolithic Graph-based reasoning A simple LangGraph workflow
You'll leave with: a clear mental model of how agents work, and your first LangGraph workflow.
Learning objectives - by the end of this module you can:
  • Explain why structure matters and enforce validation at runtime with Pydantic-AI
  • Design reasoning nodes and integrate inputs and outputs with LangGraph
  • Debug dependencies in agent workflows
  • Build extraction, classification and storage nodes in an Email-to-Task agent
What's inside
7 videos32 min
3 readings45 min
4 assignments150 min
Topics covered
Why structure matters Runtime validation Pydantic-AI schemas Reasoning nodes I/O with LangGraph Debugging dependencies Extraction nodes Classification & storage Email-to-Task agent
You'll leave with: a working Email-to-Task agent whose inputs and outputs you can trust.
Learning objectives - by the end of this module you can:
  • Compare short-term, long-term and contextual memory in agents
  • Apply memory to real-world agent use cases
  • Manage memory state and implement persistent memory with Mem0
  • Evaluate the trade-off between performance and context
What's inside
4 videos19 min
3 readings45 min
4 assignments150 min
Topics covered
Memory types & functions Real-world memory use cases Managing memory state Persistent memory with Mem0 Performance vs. context Evaluating memory performance
You'll leave with: agents that remember what matters across conversations, without slowing down.
Learning objectives - by the end of this module you can:
  • Design the architecture of a Research Assistant Agent
  • Implement summarization and recall
  • Integrate and test the agent with real research documents
  • Compare LangGraph, LangChain and Agno, and select the right framework
What's inside
7 videos30 min
3 readings45 min
4 assignments150 min
Topics covered
Agent design & architecture Summarization & recall Testing with research documents System integration LangGraph vs. LangChain vs. Agno Selecting a framework
You'll leave with: a Research Assistant Agent you built end to end, and a clear view of which framework fits which job.
05 Tools & skills

Master the frameworks that power modern AI agents.

The four agent toolkits you work through, and the skills you leave with.

Module 1

LangGraph

Graph-based workflows for agent reasoning, used from the first module to the capstone.

Module 2

Pydantic-AI

Schemas that validate what goes into and comes out of your agent.

Module 3

Mem0

Short-term, long-term and contextual memory that persists across sessions.

Module 4

LangChain & Agno

Alternative agent frameworks, compared with LangGraph on a real build.

Skills you leave with

Twelve concrete capabilities, grouped the way the work actually splits - architecture, reliability, and memory and integration.

Agent architecture

How agents are designed and orchestrated.

  • Agentic systems
  • Software architecture
  • Modular agent design
  • AI orchestration & workflows

Reliability

Agents whose output you can trust.

  • Data validation
  • Structured inputs & outputs
  • Workflow debugging
  • Testing agents

Memory & integration

Agents that remember and fit into real systems.

  • Memory management
  • Data persistence
  • Systems integration
  • Framework selection
Certificate

Proof you can link to.

Finish the graded work and Board Infinity issues a certificate through Coursera - with a public verification link and a one-click path onto your LinkedIn profile.

Issued on completion of the graded quizzes. Counts toward the Building and Deploying AI Agents specialization, completed with the two courses that follow.

On your profile in one click

Add it to your LinkedIn profile, resume or CV, where recruiters actually look.

Anyone can verify it

Its own verification link, so a hiring manager confirms it in seconds.

Earned, not attended

Awarded on graded quizzes in all four modules - it certifies what you know.

Two names on it

Board Infinity as the provider, Coursera as the platform.

06 Career impact

Every company wants AI agents. Few teams know how to architect them.

These are the roles that build on exactly this skill set.

Where these skills are in demand

AI Agent Developer

Designing and building agents that reason, act and remember - the role this course maps to most directly.

AI Engineer

Turning LLMs into reliable, production-grade applications.

LLM Application Developer

Building LLM-powered features with structured output and memory.

Machine Learning Engineer

Extending model work into agentic workflows and orchestration.

AI Solutions Architect

Choosing agent frameworks and architectures that fit the business problem.

Python Developer (AI)

Adding LangGraph, Pydantic-AI and Mem0 to a Python skill set.

Backend Engineer, AI Systems

Integrating agents with the data, storage and services around them.

AI Product Engineer

Shipping assistant-style features like research and task agents.

The honest comparison

What you're actually paying for

Every topic in this course exists free somewhere on the internet. Nobody is pretending otherwise. What a course adds is the order you learn it in, the practice that makes it stick, and something to show at the end.

What you get
This course
DIY with free content
A sequenced learning pathFoundations → reliability → memory → capstone
4 modules that build on each other
Fast-moving docs, no sequence
Practice with feedbackLearning that sticks
17 practice and graded quizzes
Copy, paste, hope it works
Real agent buildsBeyond the demo notebook
Email-to-Task and Research Assistant agents
Hello-world chains only
Expert-built contentIndustry knowledge and practice
Board Infinity's industry experts
Comment-section guesswork
Proof for recruitersVerifiable, on LinkedIn
Certificate with verification link
Nothing to show
07 The bigger path

More from the Building and Deploying AI Agents specialization.

This course stands on its own - but it's also the first step of a three-course path. Enrolling automatically enrolls you in the specialization.

You're here Course 1

AI Agent Architecture: Reasoning, Memory, and LangGraph

Modular agents in LangGraph, structured validation with Pydantic-AI, and memory with Mem0.

≈ 17 hours · 4 modules
Back to top
Course 2

Designing Multi-Agent Systems: Collaboration and Workflows

Coordinating several agents with CrewAI and Agno, sharing memory between them, and role-based pipelines.

≈ 16 hours
View course
Course 3

Deploying AI Agents: LLMs, LangGraph, and Production APIs

Connecting LLMs to LangGraph pipelines, validating outputs, and deploying with FastAPI and persistent storage.

≈ 16 hours
View course
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Take AI from build to governance - development, deployment and RAG at the system level.

View course Go deeper

Java in Machine Learning

Strengthen the machine learning, NLP and MLOps foundations underneath agent systems.

View course Go connected

API Integration - End to End Web Development

Connect your agents to the REST and GraphQL APIs and web apps around them.

View course

Board Infinity runs 270+ courses and 57 specializations on Coursera - browse the full catalogue.

Know exactly what you're signing up for.

4 modules · 28 lessons · 17 assignments · free PDF

08 FAQ

Common questions

Everything learners ask before enrolling, answered plainly.

About the course

Module one covers career scope in agentic AI, the perception-reasoning-action model, the agent lifecycle, modular versus monolithic design, and graph-based reasoning with LangGraph. Module two covers structured data and runtime validation with Pydantic-AI, LangGraph workflows, and an Email-to-Task agent. Module three covers memory types, memory state, performance trade-offs and persistent memory with Mem0. Module four brings it together in a Research Assistant Agent and compares LangGraph, LangChain and Agno.

No prior agent-building experience is required. Coursera lists this as an intermediate-level course, and basic Python and some familiarity with large language models will help you get the most from the hands-on builds.

The current enrollment options and price for your region are shown on the Coursera course page. The course is included with a Coursera Plus subscription, and Coursera's financial aid is available for eligible learners. Enrollment also includes the whole Building and Deploying AI Agents specialization track.

About 17 hours of total learning across 4 modules of roughly 5, 4, 4 and 4 hours, with 28 video lessons, 13 readings and 17 assignments. Coursera suggests 2 weeks at 10 hours a week. It's fully self-paced - deadlines flex around your schedule.

Each lesson has its own reading and quiz, and every module ends with a one-hour graded quiz - 17 assignments in all. Modules two and four are built around hands-on agents: an Email-to-Task agent and a Research Assistant Agent.

Yes - this page is Board Infinity's own overview of our course. Enrollment, payment, video lessons, grading and the certificate all happen on Coursera, where the course is hosted. Think of this page as the detailed tour before you head there.

Career & certificate

Yes. Completing the graded quizzes earns you a shareable certificate from Board Infinity, issued through Coursera with its own verification link. You can add it to your LinkedIn profile, resume or CV, and the same completed work counts toward the full specialization certificate.

It supports AI agent developer, AI engineer and LLM application developer roles, and gives machine learning engineers, Python developers and solutions architects the architecture skills to build agentic systems.

Two ways. The certificate goes in your LinkedIn Licenses & Certifications section via Coursera's one-click add. Just as valuable is naming what you built: a line like “Built an Email-to-Task agent and a Research Assistant Agent with LangGraph, Pydantic-AI validation and Mem0 memory” gives interviewers something concrete to ask about.

LangGraph runs through the whole course. You'll also use Pydantic-AI for structured validation and Mem0 for memory, and the final module compares LangGraph with LangChain and Agno so you can choose the right framework for a project.

This is the first course of the specialization. Next come Designing Multi-Agent Systems: Collaboration and Workflows (~16 hours) and Deploying AI Agents: LLMs, LangGraph, and Production APIs (~16 hours). Completing all three earns the specialization certificate.

Want someone reviewing your agent architecture?

Board Infinity 1:1 coaching pairs you with a practitioner while you take the course.

See 1:1 coaching

Build AI agents that reason, remember and work.

Four modules. Seventeen hours. LangGraph, Pydantic-AI, Mem0 and a capstone Research Assistant Agent - and a certificate to show for it.

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