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Duration 21 hours
Course Outline
Introduction to Python Environments for Agentic Development
- Configuring Python, virtual environments, and managing dependencies
- Leveraging Git and Docker for version control and environment isolation
- Best practices for ensuring reproducible environments
Overview of Agent SDKs and Frameworks
- Exploring LangChain, AutoGen, and other emerging SDKs
- Understanding agent structure and lifecycle: perception, reasoning, and action
- Comparing the capabilities and architectural styles of different SDKs
Building Functional Agents in Python
- Developing a basic agent using LangChain
- Linking agents to external tools and APIs
- Managing input/output, memory, and data persistence
Tool and API Integration
- Defining and registering tools for agent utilization
- Ensuring secure API integration and managing keys
- Incorporating external data sources and custom function calls
Agent Orchestration and Communication Patterns
- Facilitating multi-agent collaboration using AutoGen
- Implementing task delegation and planning logic
- Adopting event-driven and asynchronous orchestration techniques
Testing, Debugging, and Observability
- Testing agents with mock inputs within controlled environments
- Debugging message flows and tool invocations
- Implementing structured logging and tracking performance metrics
Deployment and Production Considerations
- Packaging and containerizing Python agent services
- Integrating with CI/CD pipelines
- Scaling, monitoring, and maintaining long-running agents
Summary and Next Steps
Requirements
- A solid understanding of Python programming and package management
- Experience working with REST APIs and JSON data structures
- Basic familiarity with asynchronous I/O concepts in Python
Target Audience
- Backend engineers
- Platform engineers
- ML engineers
