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Duration 21 hours
Course Outline
Foundations of Python Environments for Agentic Development
- Configuring Python, virtual environments, and dependency control
- Leveraging Git and Docker for version control and isolation
- Adopting best practices for reproducible development setups
Exploring Agent SDKs and Frameworks
- Examining LangChain, AutoGen, and other emerging SDKs
- Understanding agent architecture and lifecycle: perception, reasoning, and execution
- Evaluating the capabilities and design approaches of various SDKs
Developing Operational Agents in Python
- Building a basic agent using LangChain
- Linking agents to external tools and API services
- Managing input/output streams, memory retention, and persistence
Integrating Tools and APIs
- Defining and registering tools for agent utilization
- Implementing secure API connections and key management
- Utilizing external data sources and custom function invocations
Orchestrating Agents and Communication Patterns
- Facilitating multi-agent collaboration via AutoGen
- Structuring task delegation and planning logic
- Employing event-driven and asynchronous orchestration techniques
Testing, Debugging, and Observability
- Testing agents using mock inputs and controlled settings
- Troubleshooting message flows and tool executions
- Establishing structured logging and performance tracking
Deployment and Production-Ready Considerations
- Packaging and containerizing Python agent services
- Integrating services with CI/CD pipelines
- Scaling, monitoring, and maintaining long-duration agent operations
Recap and Future Directions
Requirements
- Solid grasp of Python programming and package handling
- Practical experience with REST APIs and JSON data formats
- Familiarity with Python’s asynchronous I/O mechanisms
Target Audience
- Backend Engineers
- Platform Engineers
- Machine Learning Engineers
