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 by leveraging modern SDKs and frameworks, such as LangChain and AutoGen, to construct, link, and manage agent workflows.
Delivered as an instructor-led live training (available online or on-site), this program targets intermediate backend engineers, platform engineers, and ML engineers looking to implement and orchestrate autonomous agents using Python tooling and APIs.
Upon completing this training, participants will be able to:
- Set up and configure Python-based environments tailored for agentic systems.
- Leverage popular agent SDKs like LangChain and AutoGen to build functional agents.
- Integrate tools and APIs to expand agent capabilities.
- Orchestrate multi-agent workflows and communication patterns.
- Apply 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.
Customization Options
- To request customized training for this course, please contact us to make arrangements.
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
- Exploring LangChain, AutoGen, and other emerging SDKs.
- Understanding agent structure and lifecycle: perception, reasoning, and action.
- Comparing SDK capabilities and architectural styles.
Building Functional Agents in Python
- Creating a simple agent using LangChain.
- Connecting agents to external tools and APIs.
- Managing input/output, memory, and persistence.
Tool and API Integration
- Defining and registering tools for agent usage.
- Implementing secure API integration and key management.
- Utilizing external data sources and custom function calls.
Agent Orchestration and Communication Patterns
- Facilitating multi-agent collaboration with AutoGen.
- Implementing task delegation and planning logic.
- Employing 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.
Open Training Courses require 5+ participants.
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