Building Autonomous AI Agents with LangGraph course Syllabus

Overview: This course provides a comprehensive introduction to building autonomous AI agents using the LangGraph framework. Learners will gain hands-on experience designing intelligent, LLM-powered systems capable of reasoning, memory management, and multi-step automation. The curriculum spans foundational concepts to advanced agent workflows, emphasizing real-world implementation. With a total time commitment of approximately 8–12 weeks, learners will progress through structured modules that build practical skills in agent architecture and deployment.

Module 1: Introduction to Autonomous AI Agents

Estimated time: 6 hours

• Understanding autonomous AI agents vs. traditional chatbots
• Role of large language models in agent systems
• Exploring real-world applications of AI agents
• Planning, reasoning, and execution cycles in agents

Module 2: LangGraph Framework Fundamentals

Estimated time: 10 hours

• Introduction to LangGraph architecture
• Structuring agent workflows with graphs
• Managing state and memory in agent systems
• Designing structured logic for reasoning and execution

Module 3: Building Multi-Step Agent Workflows

Estimated time: 12 hours

• Implementing planning and decision-making logic
• Constructing multi-step reasoning pipelines
• Connecting agents to external tools and APIs
• Improving task accuracy with structured flows

Module 4: Memory, Context & Tool Integration

Estimated time: 12 hours

• Implementing short-term and long-term memory systems
• Maintaining conversation context across interactions
• Integrating APIs and external tools into workflows
• Enabling dynamic actions based on tasks

Module 5: Final Project

Estimated time: 10 hours

• Design an autonomous AI agent for multi-step task execution
• Implement reasoning and decision-making workflows
• Test, refine, and demonstrate agent performance

Prerequisites

• Familiarity with Python programming
• Basic understanding of AI and machine learning concepts
• Experience with LLMs and prompt engineering recommended

What You'll Be Able to Do After

• Design and implement autonomous AI agents using LangGraph
• Orchestrate LLM-powered multi-step workflows
• Integrate memory and context management in agent systems
• Connect AI agents to external tools and APIs for automation
• Build deployable agent-based applications for real-world use cases