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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 version control and isolation.
  • Best practices for creating 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

  • 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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