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

Introduction to Python Environments for Agentic Development

• Setting up Python, virtual environments, and managing dependencies
• Utilizing Git and Docker for version control and environment isolation
• Best practices for ensuring 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 approaches

Building Functional Agents in Python

• Creating a simple 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
• Secure API integration and key management strategies
• Accessing external data sources and executing custom function calls

Agent Orchestration and Communication Patterns

• Fostering multi-agent collaboration using AutoGen
• Implementing task delegation and planning logic
• Event-driven and asynchronous orchestration models

Testing, Debugging, and Observability

• Testing agents with mock inputs in controlled environments
• Debugging message flows and tool invocations
• 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