Building an agentic AI system at scale @redhat
Building an agentic AI system at scale  @redhat
Uploaded July 2026 | Updated September 2026, 2 weeks ago
How do you build an agentic AI system that can navigate internal knowledge bases without relying entirely on expensive frontier models? As part of his keynote at Day 1 of Red Hat Summit 2026, CEO Matt Hicks describes how Red Hat engineers moved from basic chatbots to a multi-agent deep research system running directly on Red Hat infrastructure.

Watch a detailed breakdown of how Red Hat built an internal agentic system capable of parsing massive bodies of data to return useful insights. Discover the step-by-step process used to replace massive commercial models with open-weight alternatives running on virtual large language model (vLLM) infrastructure. You will learn how small language models (SLMs) can excel at highly specific, isolated tasks within a larger architecture.

Resources
๐Ÿ”— Watch the full Day 1 keynote from Red Hat Summit 2026 โ†’ youtube.com/watch?v=PgMSUGL4N5o
๐Ÿ’ก Learn more about enterprise AI use cases and trends โ†’ redhat.com/en/topics/ai
๐Ÿš€ Read more on our blog โ†’ redhat.com/en/blog

#RedHat #RHSummit #vLLM #OpenShiftAI #AIAgents #GenerativeAI #OpenWeightModels

Keywords
Red Hat, Red Hat Summit, AI Agents, Agentic Systems, Open Weight Models, vLLM, Small Language Models, SLM, Chatbots, Hallucination Detection, Multi Agent Systems, Deep Research Agent, Open Source AI, Enterprise AI, OpenShift AI, Matt Hicks
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Building an agentic AI system at scale

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