Agentic AI sees the emergence of tools designed to solve problems and make active decisions. These AI agents are poised to redefine enterprise applications, making them more integrated and intuitive. But how do you ensure these systems are trustworthy, reliable, and scalable within an enterprise environment?
Join Red Hat’s Younes Ben Brahim, Principal Product Marketing Manager, and Adel Zaalouk, Lead Product Manager, Agentic AI Workloads, as they break down the benefits and challenges surrounding the agentic AI landscape.
00:00 Introduction
00:33 Understanding agentic AI
02:06 Why agentic AI matters
03:34 Common challenges with Agentic AI
05:20 Defining an agent as a compound system: Environment, models, tools, memory, and loop
12:21 Why agents now?
14:20 Real-world agentic use cases
15:15 Agentic pipeline example
17:40 Hidden costs of agentic systems
22:21 Building with LlamaStack and hybrid flexibility
24:34 Model Context Protocol on Red Hat AI
25:29 Red Hat AI’s focus areas for agentic applications
28:02 Overview and Conclusion
🧠 Start your journey to agentic AI with Red Hat AI → https://www.redhat.com/en/products/ai/agentic-ai
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