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Kursplan
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 architectural 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 containerising Python agent services
- Integrating with CI/CD pipelines
- Scaling, monitoring and maintaining long-running agents
Summary and next steps
Krav
- Understanding of Python programming and package management
- Experience with REST APIs and JSON data structures
- Basic familiarity with asynchronous I/O in Python
Target audience
- Backend engineers
- Platform engineers
- ML engineers
21 Timer
