Agentic AI is transforming what's possible, but its rapid evolution presents real challenges for enterprise stability and reliability in production. How can you harness its power effectively? Roberto Carratala, Principal AI Platform Architect at Red Hat, breaks down the fundamental components of AI agents. In this video, Roberto discusses: ● The fast-paced evolution of Agentic AI frameworks and why the underlying platform is crucial for enterprise success. ● Tools: How agents connect to APIs, search the web, run code, and access databases to use real-time information and interact with live systems—going far beyond static training data. ● Plan & Execute: The importance of reasoning frameworks (like ReAct or Chain-of-Thought) that allow agents to think step-by-step, choose the right tools, take actions, and adapt. ● Memory: How agents remember previous interactions and workflows to maintain consistency, learn from past actions, and improve decision-making over time. Understand these core building blocks to see how Agentic AI moves beyond standalone LLMs to create AI that can truly do things in the real world. Timestamps: 0:00 - The Power & Challenges of Agentic AI 0:10 - Framework Evolution & Stability Concerns 0:36 - Component 1: Tools (APIs, real-time data, code execution) 1:04 - Component 2: Plan & Execute (Reasoning frameworks) 1:36 - Component 3: Memory (Consistency & learning) 1:51 - Conclusion: AI That Can Act Learn More About AI with Red Hat: 🧠 Explore Red Hat's approach to AI → https://www.redhat.com/en/products/ai 📖 What is Agentic AI? Read the Red Hat article → https://www.redhat.com/en/topics/ai/what-is-agentic-ai 🛠️ Agentic AI examples with Red Hat AI (GitHub) → https://github.com/rh-aiservices-bu/agentic-examples #AgenticAI #AIagents #RedHat #ArtificialIntelligence #LLM #TechExplained #EnterpriseAI

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