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Building AI Agents with Python
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Build single AI Agent and multi-agent systems using LangGraph
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Extend multi-agent systems with integrated tools to perform real-world actions, for example, for Customer Service
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Make your agentic system safe, ethical, and responsible
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Improve agentic system performance
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Complete a project to design, build, govern, test, and evaluate a working agentic AI system with a simple front end – take your project with you to demonstrate the art of the possible within your organization.
Overview
Off the shelf (OTS)
Target Audience
This course is designed for:
• Data Scientists
• Software Developers
• Machine Learning Engineers
• AI Engineers
• DevOps Engineers
Prerequisites
Participants should have:
• Experience with base Python including collections and experience with common packages for data handling such as NumPy or Pandas.
o If participants do not yet have the expected prior knowledge this can be gained through QADHPYTHON Data Handling with Python
• Desirable experience with Machine Learning development processes
o If participants do not yet have this prior knowledge this can be gained through either QAIDSDP Introduction to Data Science for Data Professionals or QADSMLP Data Science and Machine Learning with Python
Overview
Blended Learning – the best of both ways to learn.
This 3 day course blends the flexibility of self-paced learning (1 hour) with the structure of live, instructor-led sessions (3 days). You'll learn from world-class industry experts and gain practical skills to drive meaningful results in your workplace. Our digital platform also empowers you to track your progress and manage your learning journey effectively.
A mix of digital learning and live instructor-led sessions
A structured programme combining self-paced digital content with scheduled live virtual sessions. Learners build knowledge at their own pace, then apply it with expert support in interactive sessions.
Outline
01 The World of Agents
02 A Practical Introduction to Language Models
03 Working with Multiple Agents
04 Equipping Agents with Tools
05 Building AI Agents for Good: Ethics, Risk, and Responsibility
06 Improving Agentic Systems with RAG and Prompt Engineering
07 Tracing, Observability, Monitoring, & Evaluation in Agentic Systems
08 End of Course Project
• Design a complete agentic system using LLMs, tools, memory, and multi-agent coordination
• Apply ethical principles, safety checks, and tracing and evaluation strategies to an open-ended real-world task
• Integrate RAG and prompt engineering to improve relevance and performance
• Optionally, deploy a minimal frontend using Streamlit to demonstrate the system as a working prototype
Assessments
Learning outcomes are assessed through activities within this Instructor-Led course.
Delivery method
Blended
Course duration
22 hours
Competency level
Working
