How do data and AI teams ensure reliability in a rapidly evolving tech landscape?

In this episode, Lior Gavish, CTO and Co-founder of Monte Carlo Data, joins the show to uncover the growing need for data observability and reliability in the era of artificial intelligence.

Lior explains how Monte Carlo applies key DevOps principles to data pipelines and AI systems, helping teams identify and troubleshoot issues before they impact end users.

We dive into how AI is reshaping entire workflows, from software development to customer support, and why organizations are shifting from experimentation to production-scale AI deployments.

Lior shares insights on building in-house AI agents, making real-time troubleshooting up to 80% more efficient, and how his team is future-proofing data reliability as businesses lean into automated workflows at scale.

Whether you're a data professional, engineer, or tech leader, this episode explores practical strategies to build more reliable AI-driven systems.

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⏰ TIMESTAMPS:

0:00 - The AI Adoption Challenge

1:15 - Meet Lior Gavish From Monte Carlo

3:20 - Building Reliable Data Systems

10:00 - Launching Observability Agents

18:20 - Scaling With AI Use Cases

26:00 - AI's Impact On Business Value

31:10 - Effective Agent Integration In Companies

38:40 - The Software Job Market And AI

45:00 - Vibe Coding And Low Code Tools

47:30 - Ensuring AI Reliability In Production

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Leading Data Reliability in the Age of AI - A Conversation with Lior Gavish, CEO & Co founder | EP65

AI Agents Podcast