Overview

This program introduces you to Building Simple Agents with LangChain, designed for developers and AI enthusiasts seeking to create intelligent agents powered by LangChain. You’ll begin by mastering the foundational concepts of Agentic AI and the LangChain ecosystem, including understanding its architecture, key components, and capabilities. Next, you’ll dive into LLM development, focusing on prompting, context engineering, and persona design. You’ll learn to create effective prompts, engineer context to guide model behavior, and design powerful, multi-step workflows using LangChain Expression Language (LCEL). Through hands-on demonstrations, you'll build and optimize intelligent agent systems that can interact with various data sources and tools. As you progress, you’ll explore practical agent development with create_agent, and understand how to enhance agents with memory and external tools. You’ll also learn to produce structured outputs with Pydantic and TypedDict, ensuring that your agents can handle complex tasks with precision. By the end of the program, you will be able to: - Define the core principles of Agentic AI and the LangChain ecosystem. - Apply LangChain’s create_agent framework to build and customize intelligent agents. - Analyze prompt engineering and context engineering techniques to influence model behavior. - Design multi-step workflows and error-resilient pipelines using LangChain Expression Language. - Integrate external tools and synthesize structured outputs for solving complex tasks. - Optimize agents to handle real-world applications, from querying data to generating actionable insights. This program is ideal for developers, AI enthusiasts, and technical professionals looking to dive into the world of intelligent agent development. Prior experience with Python programming and basic AI concepts will help maximize your learning experience. Learners need a reliable internet connection, a modern web browser, and access to Python tools. The course uses AI tools like LangChain and Gemini API, which don't require specialized hardware. Basic knowledge of Python and AI concepts is recommended. Join us and learn to build powerful, responsive agents that can automate tasks, optimize workflows, and unlock new capabilities in AI-driven applications.

Syllabus

Getting Started with Agentic AI and the LangChain Ecosystem

Learn the fundamentals of agentic AI and how it differs from traditional prompt-based systems. Explore how autonomous agents reason, plan, and act, and examine real-world use cases where agentic systems are applied. Gain an understanding of the LangChain v1.0 ecosystem, its core components, and architecture. Build a solid technical foundation by setting up a modern AI development environment with API access and virtual environments, preparing you for hands-on agent development.

Applied LLM Development: Prompting, Context Engineering and LCEL

Discover how to work effectively with large language models using LangChain. Learn prompt engineering best practices, structured prompting techniques, and how context and persona design influence model behavior. Explore LangChain Expression Language (LCEL) to build modular, multi-step, and error-resilient workflows. Develop practical skills to design reusable pipelines that replace fragile, monolithic prompts with maintainable LLM workflows.

Practical Agent Development with LangChain

Learn how to build intelligent agents using LangChain’s create_agent framework. Explore core agent architecture patterns, multi-step reasoning, and memory integration for conversational continuity. Gain hands-on experience creating and integrating tools, and producing reliable, validated structured outputs using Pydantic and TypedDict. Build practical skills to design agents that reason, act, and interact with external systems.

Course Wrap-Up and Assessment

Consolidate your learning across the entire course and reflect on your growth in agentic AI and LangChain development. Apply your skills in a hands-on practice project, building a beginner intelligent agent that combines prompting, workflows, tools, and memory. Complete a graded end-of-course assessment to demonstrate your ability to design and reason about agent-based AI systems and prepare for more advanced agentic applications.