Hello, and welcome to this course on Agentic AI development with LangChain and LangGraph.
The future belongs to AI systems that won't just respond to requests, but will independently
reason, strategize, and act.
This shift from passive AI tools to Agentic AI is already underway.
Businesses are racing to deploy systems that can break down complex tasks, coordinate workflows,
and make dynamic decisions, fueling demand for developers skilled in LangChain and LangGraph.
Agentic AI excels in tasks requiring autonomy, tool integration, and multi-step reasoning,
with transformative applications across many domains including autonomous customer support,
financial and market analysis, healthcare and diagnostics, supply chain and logistics,
legal and compliance, e-commerce and retail, and finally, government and defense.
Now is the time to build your expertise in Agentic AI and shape the future of autonomous systems.
This hands-on course is designed for aspiring software engineers, data scientists, machine
learning engineers, AI architects, automation engineers, or someone in a related role.
Python programming skills and experience are essential for this course, as you will immediately
start building AI agents.
Familiarity with core AI concepts and the LangChain framework is highly recommended.
This course begins with a detailed look at Agentic AI, differentiating it from traditional
generative AI.
You'll explore the core components of LangGraph and understand its architecture and then look
at a crucial comparison, LangGraph versus LangChain, learning when and why to use each framework.
Through guided videos and a hands-on lab, you'll get started with LangGraph 101, building
your first stateful AI workflow and mastering the fundamentals of graph-based agent design.
Next, you'll be introduced to the exciting world of self-improving agents.
You'll explore three powerful agent architectures, Reflection Agents, Reflexion Agents, and ReAct Agents.
You'll learn how to integrate external knowledge and reason before acting.
You'll build practical examples, including a LinkedIn post-optimization agent.
Finally, the focus is on understanding the transition from single-agent to multi-agent
systems with an emphasis on how agents can collaborate, communicate, and coordinate to
solve tasks.
Through examples of Agentic RAG systems, you'll explore the foundational concept behind multi-agent
interactions.
To get the most from this course, make sure you watch the videos, go through each reading,
refer to the cheat sheets, check your understanding with practice quizzes, perform the hands-on
labs, and test your knowledge with the graded assessment.
Thank you and all the best with this course.