Architecting Agent Workflows with LangGraph

• Lectures: 140
• Duration: 10 weeks

🧭 Purpose of the Course

This course is designed to transform learners from basic LLM users into skilled agent architects capable of building sophisticated, stateful AI agents using LangGraph. The future of AI applications lies in intelligent workflows that can handle complex, multi-step tasks with precision and reliability.

The course provides  advanced, hands-on training in designing modular, observable, and scalable AI systems that can reason through complex workflows, maintain state across interactions, integrate with external tools, and deploy to production environments. Whether building customer service agents, data processing pipelines, or intelligent automation systems, this course delivers the production-ready skills needed for enterprise-grade agentic applications.

🏗️ Course Outline

1. LangGraph Overview & Setup

• What is LangGraph and its purpose in agent development
• Comparison with traditional chatbots and linear workflows
• LangGraph vs other agent frameworks and positioning
• Use cases and real-world applications
• Understanding agent workflows and state management
• Installing LangGraph and setting up development environment

2. Building Your First Graph

• Core primitives: nodes, edges, and state fundamentals
• Understanding graph execution flow and patterns
• Single-node vs multi-node architectures
• Node definition and implementation strategies
• Input/output handling and state passing
• Building a basic single-node graph with hands-on practice

3. Multi-Node Graph Design

• Sequential node execution patterns and data flow
• State transformation patterns between nodes
• Parallel execution strategies and fork/merge architectures
• Conditional branching basics and graph topology design
• Linear and branching workflow construction
• Modular design patterns and node composition

4. Conditional & Dynamic Routing

• If-Else logic implementation and switch statement patterns
• Dynamic routing strategies and condition evaluation
• Loop and iteration patterns with retry logic
• Smart routing decisions and interactive flow control
• State-based routing and error handling
• Advanced conditional patterns and performance optimization

5. Working with LLM Nodes

• Integrating LLMs into LangGraph workflows
• Prompt-driven agent design and response handling
• Message state management and context preservation
• Prompt engineering for agents and chaining LLM calls
• Interactive LLM workflows and streaming responses
• Cost management and performance optimization strategies

6. Maintaining & Customizing State

• Memory management in agents and context preservation
• Custom state schema design and data persistence
• State transformation and serialization techniques
• Cross-node state sharing and dynamic modification
• Memory optimization and state validation
• State evolution, versioning, and rollback mechanisms

7. Tool Use & Function Calls

• Integrating external tools and function calling patterns
• Tool selection strategies and orchestration
• Tool response handling and error management
• Interactive tool usage and chaining operations
• Custom tool development and async execution
• Tool performance optimization and state management

8. Debugging & Observability

• Debugging techniques for LangGraph workflows
• LangSmith integration and print tracing strategies
• Performance monitoring and error tracking
• Visualization tools and real-time monitoring
• Metrics collection and error analysis
• Interactive debugging and performance profiling

9. Deploying LangGraph Agents

• Streamlit app integration and web interface development
• API endpoint creation and security considerations
• Production deployment strategies and scalability planning
• Continuous integration and monitoring in production
• Performance optimization and containerization
• Cloud deployment options and maintenance strategies

10. Capstone Project: Modular Ordering Agent System

• System architecture design for complex agent systems
• Order processing workflow and delivery agent implementation
• Multi-agent coordination and state management
• Error handling, recovery, and user interface integration
• Performance optimization, testing, and validation
• Deployment, monitoring, and analytics implementation

🧰 Tools & Technologies Used

• Python
• LangGraph
• LangChain
• LangSmith
• OpenAI API / LLM APIs
• Streamlit
• FastAPI
• Function Tools
• Custom State Schema Libraries
• Debugging and Monitoring Tools

👨💻 Who is This Course For?

• Intermediate LLM developers ready to build complex agent systems
• Python developers wanting to enter advanced AI agent development
• LangChain users seeking to level up their workflow orchestration skills
• AI engineers building production-ready intelligent systems
• Technical leads architecting scalable AI applications

✅ Outcomes

By the end of this course, learners will be able to:

• Master LangGraph’s core primitives (nodes, edges, state) for building sophisticated workflows
• Design deterministic and dynamic stateful agent workflows with complex branching logic
• Integrate LLMs, tool calls, and external APIs seamlessly into agent systems
• Implement robust error handling, retry logic, and fallback mechanisms for production reliability
• Build multi-step agents that maintain context and memory across interactions
• Deploy and monitor production-ready agents with observability and debugging capabilities