09 Luglio 2026 · mostra note

INFORMAZIONI SU QUESTO EPISODIO

What does it take to move AI agents from demos to reliable production systems? In this episode, Hamza Tahir explores how MLOps principles are shaping the future of generative AI, covering workflows, agent harnesses, fleets, and the infrastructure needed to build durable, scalable systems. The conversation dives into open source tools, production challenges, and how ZenML's new project, Kitaru, helps developers build resilient, replayable, and observable agent systems.

Featuring:

Hamza Tahir – LinkedIn
Daniel Whitenack – Website, GitHub, X

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News tecnologiche

Tecnologia

AI e scienza dei dati

Inglese

Stati Uniti

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IN QUESTO EPISODIO

Hamza Tahir

Chris Benson

Daniel Whitenack

machine learning

deep learning

artificial intelligence

neural networks

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MOSTRA NOTE 🔗

00:00:00Welcome to Practical AI!

00:00:41Introducing the Guest

00:02:48The Evolution of MLOps and Agent Technology

00:04:35Understanding Workflows in AI and Agents

00:08:43Challenges of Durability in AI Agents

00:10:59The Future of Agents in Cloud Environments

00:13:00The Role of Harnesses in Agent Development

00:16:13The Tension Between Open and Proprietary Harnesses

00:20:45Sponsor: Framer

00:21:49Understanding Agent Architectures

00:25:15Building Durable Agent Systems

00:29:48Challenges in Agent Development

00:34:20The Future of Agent Infrastructure

00:39:03Optimizing Agent Performance

00:42:00Looking Ahead: Opportunities and Challenges

00:45:58Outro

TRASCRIZIONE 🔗

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