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Duration 21 hours
Course Outline
Foundations of Python Environments for Agentic Development
- Setting up Python, virtual environments, and managing dependencies
- Utilizing Git and Docker for version control and isolation
- Establishing best practices for reproducible development environments
Insight into Agent SDKs and Frameworks
- Examining LangChain, AutoGen, and other emerging SDKs
- Understanding agent structure and lifecycle: perception, reasoning, and action
- Comparing the capabilities and architectural styles of different SDKs
Constructing Functional Agents in Python
- Developing 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 key management
- Incorporating external data sources and custom function calls
Agent Orchestration and Communication Strategies
- Fostering multi-agent collaboration using AutoGen
- Implementing task delegation and planning logic
- Applying event-driven and asynchronous orchestration methods
Testing, Debugging, and Observability
- Testing agents using mock inputs within controlled environments
- Debugging message flows and tool invocations
- Implementing structured logging and performance metrics
Deployment and Production-Ready Considerations
- Packaging and containerizing Python agent services
- Integrating with CI/CD pipelines
- Scaling, monitoring, and maintaining long-running agent processes
Summary and Future Directions
Requirements
- A solid grasp of Python programming and package management
- Experience working with REST APIs and JSON data structures
- Fundamental knowledge of asynchronous I/O in Python
Target Audience
- Backend engineers
- Platform engineers
- ML engineers
