Check Out Our Course on How to Build AI Agents!

Dear friends,

I’m thrilled to announce my latest course: Agentic AI! This course will get you up to speed building cutting-edge agentic workflows. It is available from DeepLearning.AI here. The only prerequisite is familiarity with Python, though knowing a bit about LLMs helps too.

This self-paced course is taught in a vendor-neutral way, using raw Python — without hiding details in a framework. So you’ll learn the core concepts that you can then implement using any popular agentic AI framework, or using no framework.

Specifically, you’ll learn how to implement four key agentic design patterns:

Reflection, in which an agent examines its own output and figures out how to improve it
Tool use, in which an LLM-driven application decides which functions to call to carry out web search, access calendars, send email, write code, etc.
Planning, where you’ll use an LLM to decide how to break down a task into sub-tasks for execution, and
Multi-agent collaboration, in which you build multiple specialized agents — much like how a company might hire multiple employees — to perform a complex task.

More important, you’ll also learn best practices for building effective agents.

Having worked with many teams on many agents, I’ve found that the single biggest predictor of whether someone can build effectively is whether they know how to drive a disciplined process for evals and error analysis. Teams that don’t know how to do this can spend months tweaking agents with little progress to show for it. I’ve seen teams that spent months tuning prompts, building tools for an agent to use, etc., only to hit a performance ceiling they could not break through.

But if you understand how to put in evals and how to monitor an agent’s actions at each step (traces) to see when part of its workflow is breaking, you’ll be able to efficiently home in on which components to focus on improving. Instead of guessing what to work on, you'll let evals data guide you.

You’ll also learn to take a complex application and systematically decompose it into a sequence of tasks to implement using these design patterns. When you understand this process, you’ll also be better at spotting opportunities to build agents.

The course illustrates these concepts with many examples, such as code generation, customer service agents, and automated marketing workflows. We also build a deep research agent that searches for information, summarizes and synthesizes it, and generates a thoughtful report.

When you complete this course, you’ll understand the key building blocks of agents as well as best practices for assembling and tuning these building blocks. This will put you significantly ahead of the vast majority of teams building agents today.

Please join me in this course, and let’s build some amazing agents!

Keep building,

Andrew