Python for Autonomous Agents: Tooling, SDKs & Orchestration Training Course
Python serves as the foundational language for developing and orchestrating autonomous AI agents. This course emphasizes practical implementation, utilizing modern SDKs and frameworks like LangChain and AutoGen to construct, link, and manage agent workflows.
This instructor-led live training (available online or on-site) is designed for intermediate-level backend, platform, and ML engineers looking to implement and orchestrate autonomous agents using Python tooling and APIs.
Upon completing this training, participants will be capable of:
- Setting up and configuring Python-based environments for agentic systems.
- Leveraging popular agent SDKs such as LangChain and AutoGen to develop functional agents.
- Integrating tools and APIs to enhance agent capabilities.
- Orchestrating multi-agent workflows and communication patterns.
- Applying best practices for debugging, testing, and maintaining agentic codebases.
Course Format
- Interactive lectures and discussions.
- Hands-on programming exercises and live demonstrations.
- Practical projects focused on building end-to-end agent workflows.
Course Customization Options
- To request customized training for this course, please contact us to arrange details.
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
- 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
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