Why standardization is the key to agentic AI success: How a unified platform spurs innovation

Why standardization is the key to agentic AI success: How a unified platform spurs innovation Understanding the building blocks of an intelligent agent An example of agentic AI in action A streamlined approach for development and production Gaining operational consistency for AI workloads Learn more The adaptable enterprise: Why AI readiness is disruption readiness About the authors Younes Ben Brahim Carlos Condado Will McGrath Roberto Carratalá Philip Hayes Cedric Clyburn More like this Blog post Blog post Original podcast Original podcast Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share You're tasked with bringing AI into your organization, but the journey from a proof-of-concept to a production-ready application is often a maze of fragmented tools and complex integrations, especially for agentic AI use cases. The real challenge isn't just the technology—it's building an AI strategy that is scalable, reliable, and manageable. Standardization is a primary factor in meeting this challenge, primarily by reducing complexity and increasing efficiency. By combining Model Context Protocol (MCP) and Llama Stack on a platform like Red Hat OpenShift AI , you can create a unified and portable environment for your AI applications. OpenShift AI can also support the integration of any other agentic frameworks and components you like, including tools like LangChain, LangGraph, and CrewAI, among others. Red Hat's approach can help simplify development, streamline operations, and give your enterprise the tools it needs to empower your multiple teams, like AI developers and data scientists to turn your AI vision into a reality within a trusted and consistent environment. To build resilient AI-enabled applications, you need a foundation that enables intelligent agents to access and use your enterprise's specific tools and data. This requires 2 key components: a standardized way to expose your services and a unified framework for interacting with them. This is the essence of agentic AI , where an autonomous system can determine the best course of action and tools needed to achieve a goal. The combination of MCP and Llama Stack provides the framework for these agents to operate effectively. MCP is an open protocol that standardizes how your AI systems integrate with external tools and data sources. It has been described as being a "USB-C port for AI applications.