Agent Development - Implementation Track¶
⏱️ Estimated reading time: 3 minutes
This track provides hands-on guidance for building sophisticated agentic AI systems using modern frameworks and tools. You'll learn to implement, optimize, and deploy production-ready AI agents.
Table of Contents¶
| Chapter | Title | Description |
|---|---|---|
| Chapter 1 | Introduction to Agentic AI | Overview of agentic systems and development frameworks |
| Chapter 2 | LangChain: The Foundation for Agents | Building agent foundations with LangChain framework |
| Chapter 3 | LangGraph: Orchestrating Complex Agentic Behavior | State management and complex workflow orchestration |
| Chapter 4 | Designing an Agentic System: LangChain + LangGraph in Action | Integrated approach to building complete agent systems |
| Chapter 5 | Advanced Agent Optimization with DSPy | Optimizing agent performance and prompt engineering |
| Chapter 6 | State Management and Persistence with Checkpointers | Managing agent memory and state across sessions |
| Chapter 7 | Debugging and Tracing with LangSmith | Monitoring, debugging, and improving agent performance |
| Chapter 8 | Conclusion | Best practices and deployment considerations |
| Chapter 9 | References and Further Reading | Additional resources and advanced topics |
Learning Path¶
This track is designed for hands-on learning. Start with Chapter 1 for an overview, then work through each chapter sequentially. Each chapter includes practical examples and code implementations.
Prerequisites¶
- Programming experience (Python recommended)
- Basic understanding of AI/ML concepts
- Familiarity with APIs and web services
- Completion of AI Systems track (recommended but not required)
Tools You'll Use¶
- LangChain: Agent foundation framework
- LangGraph: State management and orchestration
- DSPy: Prompt optimization and evaluation
- LangSmith: Debugging and monitoring
- Python: Primary development language
Ready to start building? Begin with Chapter 1: Introduction to Agentic AI →