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Duration 21 hours
Course Outline
Introduction to Python Environments for Agentic Development
- Setting up Python, virtual environments, and dependency management.
- Utilizing Git and Docker for version control and isolation.
- Best practices for creating 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 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 persistence.
Tool and API Integration
- Defining and registering tools for agent utilization.
- Secure API integration and key management.
- Incorporating external data sources and custom function calls.
Agent Orchestration and Communication Patterns
- Multi-agent collaboration using AutoGen.
- Task delegation and planning logic.
- Event-driven and asynchronous orchestration.
Testing, Debugging, and Observability
- Testing agents using mock inputs and controlled environments.
- Debugging message flow and tool invocation.
- Implementing structured logging and 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 knowledge of asynchronous I/O in Python.
Target Audience
- Backend engineers.
- Platform engineers.
- ML engineers.
