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Duration 21 hours
Course Outline
Introduction to Python Environments for Agentic Development
- Configuring Python, virtual environments, and dependency management
- Leveraging Git and Docker for version control and isolation
- Best practices for maintaining reproducible environments
Overview of Agent SDKs and Frameworks
- LangChain, AutoGen, and other emerging SDKs
- Agent structure and lifecycle: perception, reasoning, and action
- Evaluating SDK capabilities and architectural patterns
Building Functional Agents in Python
- Developing 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 usage
- Secure API integration and key management
- Utilizing external data sources and custom function calls
Agent Orchestration and Communication Patterns
- Facilitating multi-agent collaboration with AutoGen
- Task delegation and planning logic
- Event-driven and asynchronous orchestration
Testing, Debugging, and Observability
- Testing agents using mock inputs and controlled settings
- Debugging message flow and tool execution
- Implementing structured logging and performance metrics
Deployment and Production Considerations
- Packaging and containerizing Python agent services
- Integrating with CI/CD pipelines
- Scaling, monitoring, and sustaining long-running agents
Summary and Next Steps
Requirements
- A solid grasp of Python programming and package management
- Hands-on experience with REST APIs and JSON data structures
- Fundamental knowledge of asynchronous I/O in Python
Audience
- Backend engineers
- Platform engineers
- ML engineers
