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Duration 21 hours
Course Outline
Introduction to Python Environments for Agentic Development
- Establishing Python, virtual environments, and dependency management.
- Leveraging Git and Docker for version control and isolation.
- Adopting best practices for reproducible 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 styles.
Building Functional Agents in Python
- Creating 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.
- Implementing secure API integration and key management.
- Incorporating external data sources and custom function calls.
Agent Orchestration and Communication Patterns
- Facilitating multi-agent collaboration with AutoGen.
- Structuring task delegation and planning logic.
- Implementing event-driven and asynchronous orchestration.
Testing, Debugging, and Observability
- Testing agents using mock inputs and controlled environments.
- Debugging message flow and tool invocation processes.
- Implementing structured logging and tracking performance metrics.
Deployment and Production Considerations
- Packaging and containerizing Python agent services.
- Integrating services with CI/CD pipelines.
- Scaling, monitoring, and maintaining long-running agents.
Summary and Next Steps
Requirements
- A solid grasp of Python programming and package management.
- Experience with REST APIs and JSON data structures.
- Basic knowledge of asynchronous I/O within Python.
Audience
- Backend engineers.
- Platform engineers.
- ML engineers.
