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Duration 21 hours
Course Outline
Foundations of Python Environments for Agentic Development
- Installation of Python, virtual environments, and dependency management
- Leveraging Git and Docker for version control and environment isolation
- Strategies for ensuring reproducible development environments
Exploring Agent SDKs and Frameworks
- In-depth look at LangChain, AutoGen, and other emerging SDKs
- Understanding agent structure and lifecycle: perception, reasoning, and action
- Comparison of SDK capabilities and architectural approaches
Developing Functional Agents in Python
- Building a basic agent using LangChain
- Linking agents to external tools and APIs
- Managing input/output, memory, and data persistence
Integrating Tools and APIs
- Defining and registering tools for agent utilization
- Ensuring secure API integration and effective key management
- Utilizing external data sources and custom function calls
Orchestrating Agents and Communication Patterns
- Facilitating multi-agent collaboration with AutoGen
- Implementing task delegation and planning logic
- Managing event-driven and asynchronous orchestration
Testing, Debugging, and Observability
- Testing agents using mock inputs within controlled environments
- Debugging message flow and tool invocation processes
- Implementing structured logging and performance metrics
Deployment and Production Readiness
- Packaging and containerizing Python agent services
- Integrating agent deployments with CI/CD pipelines
- Scaling, monitoring, and maintaining long-running agent processes
Conclusion and Future Directions
Requirements
- Solid grasp of Python programming and package management
- Practical experience with REST APIs and JSON data structures
- Foundational knowledge of asynchronous I/O in Python
Target Audience
- Backend engineers
- Platform engineers
- ML engineers
