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, leveraging modern SDKs and frameworks such as LangChain and AutoGen to build, connect, and manage agent workflows effectively.
Delivered as an instructor-led live training (available online or on-site), this programme is designed for intermediate-level backend engineers, platform engineers, and ML engineers seeking to implement and orchestrate autonomous agents using Python tooling and APIs.
Upon completion of this training, participants will be able to:
- Set up and configure Python-based environments tailored for agentic systems.
- Utilise popular agent SDKs, including LangChain and AutoGen, to develop functional agents.
- Integrate tools and APIs to enhance 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 a customized training version of this course, please contact us to arrange.
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 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 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.
- Utilizing 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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Python for Autonomous Agents: Tooling, SDKs & Orchestration Training Course - Enquiry
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