About This Course
Artificial intelligence and machine learning come with huge benefits, but also pitfalls. What once required specialized skills, large teams, complex workflows and deep algorithmic tuning can now be accelerated, automated or simulated using AI/ML tools. As these tools become more accessible, it’s increasingly important to know how to reimagine tooling in the agentic AI paradigm while applying rigorous engineering standards and governance guardrails.
In this course, you’ll learn to harness the power of AI to build agentic systems that meet production best practices. Using foundation models, you’ll build a native AI agent from scratch. Then, you’ll build a retrieval-augmented generation (RAG) pipeline from scratch, applying tools at every step to build scalable production infrastructure, automate workflows and speed up your work.
Designed For
Software engineers and Python programmers who want to build agentic systems.
What You'll Learn
How to build AI agents from the ground up — including reason-and-act loops and tool calling
Techniques for connecting agents to tools and data with MCP and packaging reusable agent workflows as Skills
Approaches to context engineering and evaluating agentic applications to improve reliability and reduce hallucinations
Agent design patterns for building and scaling multi-agent systems with LangChain and LangGraph
Strategies for security, deployment, and scaling
Earn a Digital Badge
After successfully completing this course, you can claim a digital achievement badge that can be shared on LinkedIn and other social media sites. Learn more about digital badges.
Our Enrollment Coaches Are Here to Help
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This program is intended for professional development and is not designed to meet educational requirements for professional licensure or certification.
