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Course Outline
Introduction to Python Environments for Agentic Development
- Establishing Python setups, virtual environments, and dependency management
- Utilizing Git and Docker for version control and environment isolation
- Adopting best practices for reproducible development environments
Overview of Agent SDKs and Frameworks
- Exploring LangChain, AutoGen, and other emerging SDKs
- Understanding agent structure and lifecycle: perception, reasoning, and action
- Evaluating SDK capabilities and architectural approaches
Building Functional Agents in Python
- Developing a basic agent using LangChain
- Connecting agents to external tools and APIs
- Managing input/output, memory, and data persistence
Tool and API Integration
- Defining and registering tools for agent utilization
- Implementing secure API integration and key management
- Accessing external data sources and executing custom function calls
Agent Orchestration and Communication Patterns
- Facilitating multi-agent collaboration using AutoGen
- Managing task delegation and planning logic
- Implementing 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 metric tracking
Deployment and Production Considerations
- Packaging and containerizing Python agent services
- Integrating agent services with CI/CD pipelines
- Scaling, monitoring, and maintaining long-running agent instances
Summary and Next Steps
Requirements
- A solid grasp of Python programming and package management
- Proficiency with REST APIs and JSON data structures
- Basic understanding of asynchronous I/O in Python
Target Audience
- Backend engineers
- Platform engineers
- ML engineers
21 Hours
