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Duration 21 hours
Course Outline
Introduction to Python Environments for Agentic Development
- Configuring Python, virtual environments, and dependency management
- Leveraging Git and Docker for version control and environment isolation
- Adopting best practices for reproducible development environments
Overview of Agent SDKs and Frameworks
- Examining LangChain, AutoGen, and other emerging SDKs
- Understanding agent structure and lifecycle: perception, reasoning, and action
- Comparing SDK capabilities and architectural approaches
Building Functional Agents in Python
- Developing a basic agent using LangChain
- Linking agents to external tools and APIs
- Managing input/output streams, memory, and persistence
Tool and API Integration
- Defining and registering tools for agent utilization
- Ensuring secure API integration and robust key management
- Incorporating external data sources and custom function calls
Agent Orchestration and Communication Patterns
- Facilitating multi-agent collaboration with AutoGen
- Implementing task delegation and planning logic
- Designing event-driven and asynchronous orchestration models
Testing, Debugging, and Observability
- Testing agents via mock inputs and controlled environments
- Debugging message flows and tool invocations
- Establishing structured logging and performance metric tracking
Deployment and Production Considerations
- Packaging and containerizing Python agent services
- Integrating agents into CI/CD pipelines
- Scaling, monitoring, and maintaining long-running agent processes
Summary and Next Steps
Requirements
- Solid understanding of Python programming and package management
- Proficiency with REST APIs and JSON data structures
- Fundamental knowledge of asynchronous I/O operations in Python
Target Audience
- Backend Engineers
- Platform Engineers
- ML Engineers
