LLM Agents: When to Use Them (and When Not To)

Hosted by Hugo Bowne-Anderson and Stefan Krawczyk

Tue, Feb 25, 2025

12:00 AM UTC (30 minutes)

Virtual (Zoom)

Free to join

207 students

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Building LLM Applications for Data Scientists and Software Engineers
Hugo Bowne-Anderson and Stefan Krawczyk

What you'll learn

Understand what LLM agents are and when to use them.

Structure agentic workflows with tool use and more.

Debug and evaluate agentic systems for reliability.

Why this topic matters

LLM agents are everywhere, but do you need them? Multi-agent systems sound promising but add orchestration, debugging, and observability challenges. In this Lightning Lesson, you’ll learn when agents make sense, how to build structured agent workflows, and how to avoid unnecessary complexity. We’ll also touch on debugging techniques to keep agentic workflows reliable and scalable.

You'll learn from

Hugo Bowne-Anderson

Podcaster, Educator, DS & ML expert

Hugo Bowne-Anderson is an independent data and AI consultant with extensive experience in the tech industry. He is the host of the industry Vanishing Gradients, where he explores cutting-edge developments in data science and artificial intelligence. As a data scientist, educator, evangelist, content marketer, and strategist, Hugo has worked with leading companies in the field. His past roles include Head of Developer Relations at Outerbounds, a company committed to building infrastructure for machine learning applications, and positions at Coiled and DataCamp, where he focused on scaling data science and online education respectively. Hugo's teaching experience spans from institutions like Yale University and Cold Spring Harbor Laboratory to conferences such as SciPy, PyCon, and ODSC. He has also worked with organizations like Data Carpentry to promote data literacy. His impact on data science education is significant, having developed over 30 courses on the DataCamp platform that have reached more than 3 million learners worldwide. Hugo also created and hosted the popular weekly data industry podcast DataFramed for two years. Committed to democratizing data skills and access to data science tools, Hugo advocates for open source software both for individuals and enterprises.

Stefan Krawczyk

13+years in MLOps: Ex-Stitch Fix, Ex-Nextdoor, Ex-LinkedIn

Stefan Krawczyk is the co-founder and CEO of DAGWorks, an open-source company driving two projects: Hamilton & Burr, whose mission to empower developers to build reliable AI agents & applications. He is a Y Combinator alum, StartX alum, and a Stanford graduate with a Master of Science in Computer Science with Distinction in Research. He has over thirteen years of experience in building and leading data & ML-related systems and teams, at companies like Stitch Fix, Idibon, Nextdoor, and Linkedin, his passion is to make others more successful with data by bridging the engineering gap between data science, machine learning, artificial intelligence, and the business.

Previously at

LinkedIn
Yale University
Outerbounds
Stanford University
Stitch Fix

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