Python for Autonomous Agents: Tooling, SDKs & Orchestration Training Course

Introduction to Python Environments for Agentic Development

• Establishing Python, virtual environments, and managing dependencies
• Utilizing Git and Docker for version control and environment isolation
• Best practices for creating reproducible environments

Overview of Agent SDKs and Frameworks

• LangChain, AutoGen, and other emerging SDKs
• 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 strategies
• Incorporating external data sources and custom function calls

Agent Orchestration and Communication Patterns

• Facilitating multi-agent collaboration with AutoGen
• Logic for task delegation and planning
• Event-driven and asynchronous orchestration methods

Testing, Debugging, and Observability

• Testing agents using mock inputs and controlled environments
• Debugging message flows and tool invocations
• Implementing structured logging and performance metrics

Deployment and Production Considerations

• Packaging and containerizing Python agent services
• Integration with CI/CD pipelines
• Scaling, monitoring, and maintaining long-running agents

Summary and Next Steps