Building Autonomous AI Agents with LangGraph course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This course provides a comprehensive introduction to building autonomous AI agents using the LangGraph framework. Designed for developers, it covers core concepts of AI agent architectures, reasoning, planning, and multi-step automation. Through hands-on exercises and real-world applications, learners will gain practical experience in designing intelligent systems that interact with tools and APIs. The course spans approximately 6-8 weeks with a total time commitment of 30-40 hours, combining theory and implementation.
Module 1: Introduction to Autonomous AI Agents
Estimated time: 6 hours
- Understanding AI agents vs. traditional chatbots
- Role of large language models in autonomous systems
- Real-world applications of AI agents in automation
- Core concepts: reasoning, planning, and task execution
Module 2: LangGraph Framework Fundamentals
Estimated time: 10 hours
- Introduction to the LangGraph architecture
- Understanding nodes, edges, and state management
- Structuring AI agent workflows using graphs
- Designing task pipelines for reasoning and decision-making
Module 3: Building Multi-Step AI Agent Workflows
Estimated time: 14 hours
- Implementing planning and reasoning strategies
- Creating multi-step automation pipelines
- Connecting agents to APIs and external tools
- Improving reliability through structured workflows
Module 4: Memory, Context & Tool Integration
Estimated time: 10 hours
- Implementing memory systems in AI agents
- Maintaining conversation context and task history
- Integrating external APIs and tools into workflows
- Enabling dynamic and adaptive task execution
Module 5: Final Project
Estimated time: 8 hours
- Design a multi-step AI agent workflow
- Implement reasoning and decision logic
- Integrate external tools and services
Prerequisites
- Basic programming knowledge (Python preferred)
- Familiarity with AI and machine learning concepts
- Experience with APIs and web services
What You'll Be Able to Do After
- Design and implement autonomous AI agents using LangGraph
- Orchestrate large language models for complex reasoning tasks
- Build multi-step workflows with planning and decision logic
- Integrate AI agents with external tools and APIs
- Apply memory and context management in agent systems