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Duration 21 hours
Course Outline
Introduction to Python Environments for Agentic Development
- Configuring Python, virtual environments, and dependency management
- Utilizing Git and Docker for version control and 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 styles
Building Functional Agents in Python
- Developing a simple agent using LangChain
- Connecting agents to external tools and APIs
- Managing input/output, memory, and persistence
Tool and API Integration
- Defining and registering tools for agent utilization
- Securing API integrations and managing keys
- Incorporating external data sources and custom function calls
Agent Orchestration and Communication Patterns
- Fostering multi-agent collaboration with AutoGen
- Implementing task delegation and planning logic
- Employing event-driven and asynchronous orchestration
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
- Integrating with CI/CD pipelines
- Scaling, monitoring, and maintaining long-running agents
Summary and Next Steps
Requirements
- Solid understanding of Python programming and package management
- Proficiency with REST APIs and JSON data structures
- Basic knowledge of asynchronous I/O in Python
Target Audience
- Backend engineers
- Platform engineers
- ML engineers
