Found it useful. The flow was logical, and the illustrative examples helped solidify the ideas. Could have used a bit more depth.
Agentic AI Engineering with LangChain and LangGraph
Develop autonomous AI agents and sophisticated multi-agent systems using LangChain and LangGraph to solve complex tasks with Python.
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About this course
Modern AI is moving beyond simple chat interfaces toward autonomous agents that can reason, plan, and execute tasks. This course provides a clear path to understanding and building these sophisticated systems using the industry-standard LangChain and LangGraph frameworks.
You will gain the skills to engineer production-ready agentic systems that use external tools and manage complex workflows. By the end of this course, you will be able to design AI architectures that autonomously solve real-world problems.
What you'll learn:
- Understand the core principles of agentic AI and autonomous systems
- Build multi-agent architectures using LangGraph and LangChain
- Implement advanced Retrieval-Augmented Generation (RAG) for data-driven agents
- Configure tool calling and Model Context Protocol (MCP) integrations
- Design complex reasoning workflows with chain-of-thought and ReAct prompting
- Develop production-ready AI applications using Python
The course begins with foundational terminology and prompt engineering theory before progressing to practical implementation. You will explore how to create search agents and documentation assistants, moving from basic concepts to integrated agentic systems.
This course is designed for software developers and data professionals looking to enter the field of AI engineering. While familiarity with Python is helpful, no prior experience with LangChain is required.
Start building the next generation of autonomous AI today.
Course contents
5 modules · 28 lessons · 3h 7m
1
Foundations of Agentic AI
Understanding what agents are and mastering basic programming tools.
- The Era of Agents: Introduction
Explaining the difference between a standard chat and an autonomous agent.6 min - AI Engineer's Glossary
Key terminology: LLM, Tokens, Agents, Environment.6 min - Prompt Engineering Theory
How to design instructions that force the model to plan.7 min - The LangChain Ecosystem
An overview of the library and its role in building AI applications.7 min - Your First Agent in Python
Setting up the environment and running a simple chain.7 min - Architecture of Autonomous Systems
The operational diagram of an agent's decision loop.7 min
2
Reasoning and Tool Interaction
Teaching the agent logical thinking and how to use external functions.
- Chain of Thought (CoT)
Implementing step-by-step techniques in the agent's thought process.7 min - The ReAct Framework
Combining reasoning (Reason) and action (Act) in a single prompt.7 min - Tool Calling in Practice
How to teach the model to select and call Python functions.6 min - MCP Integrations
Using the Model Context Protocol to connect with external data.7 min - Creating Custom Tools
Defining custom tools that the agent can understand.7 min - Tool Error Handling
How the agent should respond to failures in external systems.6 min
3
Advanced RAG for Agents
Building agents that can intelligently search and analyze data.
- RAG vs Agentic RAG
Why static search is not enough for autonomous systems.6 min - Building a Search Agent
An agent using Google Search or Tavily to find information.7 min - Documentation Assistant
Creating an agent that 'talks' to your PDF and Markdown files.7 min - Self-RAG and Correction
Techniques for the agent to verify if the retrieved data is correct.7 min - Data-Driven Agents
Integrating the agent with vector databases for long-term memory.7 min
4
Complex Flows with LangGraph
Designing multi-agent systems with full control over state and logic.
- Introduction to LangGraph
Why graphs are better than linear chains for complex tasks.6 min - Nodes and Edges
Defining workflow logic within the graph.7 min - State Management
How to maintain context and memory in long-running agent processes.7 min - Multi-Agent Architectures
Collaboration of multiple agents (e.g., Researcher and Editor) on a single task.7 min - Hierarchy and Orchestration
A 'Supervisor' model managing the work of subordinate agents.7 min - Planning and Reflection
A loop where the agent evaluates its work and corrects errors.7 min
5
Production and Troubleshooting
Transforming a prototype into a stable application ready for users.
- Cost Optimization
How to build intelligent systems without going bankrupt on tokens.6 min - Monitoring with LangSmith
Debugging and tracing agent thought processes in production.6 min - Agent Security
Restricting access and preventing prompt injections or escapes.6 min - Case Study: Real-World Problem
Analyzing an architecture that solves a specific business task.7 min - Application Deployment
Running the agent as an API or a web interface in Python.7 min
Final project
Autonomous Research Team
Build a multi-agent system that takes a general topic, searches the internet, analyzes local documents, and then generates a structured final report.
What you'll get
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Certificate of completion
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Short & focused
3h 7m of practical content
Certificate of completion
Every course you complete on PickAClass issues a credential like this — original, with its own code, verifiable by URL, and detailed about what was actually demonstrated.
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Certificate of Mastery
This certifies that
Name Surname
has successfully demonstrated mastery of
Agentic AI Engineering with LangChain and LangGraph
Skills demonstrated
✓
Behavioral pattern analysis
Foundational
1.2 hrs
✓
Decision-architecture frameworks
Proficient
1.4 hrs
✓
A/B test design
Proficient
1.7 hrs
✓
Behavioral copywriting
Advanced
1.9 hrs
P
PickAClass — Name Surname
Agentic AI Engineering with LangChain and LangGraph
Page 2 of 2
Performance detail
Coursework summary
Lessons completed
14 / 14
Practice questions
26 / 28
Assignments submitted
4 (avg 4.5 / 5)
Capstone project
Reviewed — 4.6 / 5
Total practice
6.2 hrs
Performance benchmark
Cohort rank
Top 12% of 1,625
Time to completion
11 days (median: 22)
Mastery score
91 / 100
Practice-question score
94%
Skill verification
Verified Skill Path
Reviews (4)
Fantastic course. The examples used were spot on and really helped solidify the concepts. My understanding has improved dramatically.
It's a solid course. The structure is logical and most of the examples were helpful. Could use a few more real-world scenarios though.
Really enjoyed this course. The structure made it easy to follow, and the content was super engaging.
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