Making LLM Agents Observable & Debuggable
Hosted by Hugo Bowne-Anderson and Claire Longo
Thu, Oct 9, 2025
11:00 PM UTC (30 minutes)
Virtual (Zoom)
Free to join
By continuing, you agree to Maven's Terms and Privacy Policy.
Go deeper with a course


Thu, Oct 9, 2025
11:00 PM UTC (30 minutes)
Virtual (Zoom)
Free to join
Go deeper with a course


What you'll learn
How to debug and monitor agent behaviour in real-time
Work with human annotations and LLM's-as-a-judge
Using MCPs to level-up your vibe coding with telemetry
Start building today with open-source cookbooks
Why this topic matters
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.
Claire Longo
AI Researcher at Comet | Mathematician | Startup Advisor | ex-Arize AI 📈
Claire is an AI leader, a MLOps and data science practitioner, and an advocate and mentor for women in the AI industry. She started her career as a statistician and worked her way up to a data scientist role at Trunk Club where she specialized in building personalization algorithms and outfit recommenders. After personally feeling the challenges of bringing models to production, she focused her career on MLOps and LLMOps best practices. She moved to Twilio and then Opendoor where she built and led engineering teams and platform teams focused on researching and deploying models at scale. She holds a master’s in statistics and a bachelor’s in applied mathematics.
By continuing, you agree to Maven's Terms and Privacy Policy.