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Course Outline

Introduction to Python Environments for Agentic Development

  • Setting up Python, virtual environments, and dependency management
  • Using Git and Docker for versioning and isolation
  • Best practices for reproducible environments

Overview of Agent SDKs and Frameworks

  • LangChain, AutoGen, and other emerging SDKs
  • Agent structure and lifecycle: perception, reasoning, and action
  • Comparing SDK capabilities and architecture styles

Building Functional Agents in Python

  • Creating a simple agent with LangChain
  • Connecting agents to external tools and APIs
  • Handling input/output, memory, and persistence

Tool and API Integration

  • Defining and registering tools for agent use
  • Secure API integration and key management
  • Using external data sources and custom function calls

Agent Orchestration and Communication Patterns

  • Multi-agent collaboration using AutoGen
  • Task delegation and planning logic
  • Event-driven and asynchronous orchestration

Testing, Debugging, and Observability

  • Testing agents with mock inputs and controlled environments
  • Debugging message flow and tool invocation
  • Implementing structured logging and performance metrics

Deployment and Production Considerations

  • Packaging and containerizing Python agent services
  • Integrating with CI/CD pipelines
  • Scaling, monitoring, and maintaining long-running agents

Summary and Next Steps

Requirements

  • An understanding of Python programming and package management
  • Experience with REST APIs and JSON data structures
  • Basic familiarity with asynchronous I/O in Python

Audience

  • Backend engineers
  • Platform engineers
  • ML engineers
 21 Hours

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