Mirantis Introduces MCP AdaptiveOps to Accelerate Enterprise Adoption of Agentic Infrastructure
Mirantis, a leader in Kubernetes-native AI infrastructure, has announced the launch of MCP AdaptiveOps, a groundbreaking solution designed to help enterprises build and operate Model Context Protocol (MCP) servers at scale. This new offering provides engineering teams with a safe, reliable, and future-proof framework to deploy MCP servers that are production-ready, backed by robust service levels, and adaptable to the rapidly evolving standards and components within the MCP ecosystem.
As agentic AI infrastructure becomes increasingly critical for enterprises, the need for a standardized protocol like MCP is undeniable. However, the fast-moving nature of this ecosystem—comprising registries, gateways, and large language model (LLM) routers—presents challenges in terms of interoperability, compliance, and long-term viability. MCP AdaptiveOps addresses these challenges by abstracting away uncertainty, enabling organizations to adopt MCP with confidence while ensuring their systems remain flexible and aligned with emerging standards.
The Growing Importance of Agentic AI Infrastructure
According to Gartner, Inc., more than 40% of agentic AI projects are projected to be canceled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls. In this early stage of agentic AI adoption, Gartner recommends that enterprises pursue such initiatives only when they deliver clear value or return on investment (ROI).
This cautious approach underscores the importance of having a reliable foundation for implementing agentic AI solutions. As Randy Bias, Vice President of Open Source Strategy and Technology at Mirantis, explains, “MCP is rapidly becoming the standard for connecting enterprise services into agentic infrastructure, but the ecosystem is still shifting. Mirantis has a proven track record of helping customers successfully adopt and thrive with open-source projects like Kubernetes and OpenStack—striking the right balance between opinionated productization and flexibility during the early stages of adoption. With MCP AdaptiveOps, we’re giving engineering teams the tools they need to build today without locking themselves into fragile assumptions about tomorrow.”
By providing the reliability and adaptability required to navigate the evolving MCP landscape, Mirantis is accelerating enterprise adoption of agentic infrastructure while minimizing risks and ensuring long-term success.
Key Features of MCP AdaptiveOps
MCP AdaptiveOps is designed to address the unique challenges faced by enterprises as they implement and scale MCP servers. The solution offers three core capabilities:
- Audits of Existing MCP Servers
For organizations already using MCP servers, Mirantis provides comprehensive audits to assess their current implementations. These audits include detailed recommendations to align existing MCP servers with both external ecosystems and internal enterprise requirements. This ensures that organizations can optimize their systems for performance, security, and compliance while remaining adaptable to future changes. - Greenfield Builds of New MCP Servers
For enterprises starting from scratch, MCP AdaptiveOps offers clean abstractions that enable the construction of new MCP servers with built-in flexibility. By leveraging these abstractions, organizations can future-proof their deployments, ensuring that their systems remain compatible with emerging standards and components as the MCP ecosystem matures. - Operational Support and SLAs
To ensure stability, security, and continuous evolution, Mirantis provides operational support and service-level agreements (SLAs) for MCP servers. This guarantees that enterprises can rely on their MCP infrastructure to meet production demands while adapting seamlessly to changes in the broader ecosystem.
A Future-Proof Framework for Agentic AI Success
The rapid evolution of the MCP ecosystem presents both opportunities and challenges for enterprises. While MCP is poised to become the de facto standard for connecting enterprise services to agentic infrastructure, the shifting landscape can create uncertainty for organizations investing in this technology. Without a clear roadmap, enterprises risk building systems that are incompatible with future standards or locked into outdated assumptions.
MCP AdaptiveOps eliminates these risks by providing a framework that balances structure and flexibility. By abstracting away the complexities of the current ecosystem, Mirantis empowers engineering teams to focus on delivering value rather than navigating technical uncertainties. This approach not only accelerates the deployment of MCP servers but also ensures that they remain adaptable and compliant as the ecosystem evolves.
Why Enterprises Need MCP AdaptiveOps
The rise of agentic AI infrastructure is transforming how enterprises leverage AI to drive innovation and efficiency. However, the complexity of integrating AI models into enterprise workflows requires a robust and standardized protocol like MCP. By adopting MCP AdaptiveOps, organizations can overcome key barriers to successful implementation, including:
- Interoperability: Ensuring seamless integration with existing systems and emerging standards.
- Compliance: Meeting regulatory and enterprise-specific requirements while maintaining flexibility.
- Scalability: Building systems that can grow alongside increasing demands and evolving technologies.
- Reliability: Delivering stable, secure, and high-performing MCP servers that meet production needs.
With MCP AdaptiveOps, Mirantis is addressing these challenges head-on, enabling enterprises to adopt agentic AI infrastructure with confidence.
Mirantis’ Proven Expertise in Open Source Innovation
Mirantis has a long history of helping enterprises adopt and succeed with open-source technologies. From Kubernetes to OpenStack, the company has consistently provided solutions that strike the right balance between structured productization and the flexibility needed during the early stages of adoption. This expertise positions Mirantis as a trusted partner for organizations navigating the complexities of the MCP ecosystem.
“Mirantis understands the challenges of adopting emerging technologies,” said Bias. “With MCP AdaptiveOps, we’re bringing that same expertise to the world of agentic AI infrastructure. Our goal is to provide enterprises with the tools they need to accelerate adoption, reduce risks, and achieve long-term success.”
Securing the Future of Model Context Protocol
As the demand for agentic AI infrastructure continues to grow, the role of MCP in connecting enterprise services to this infrastructure will only become more critical. MCP AdaptiveOps lays the groundwork for mass enterprise adoption by providing a secure, scalable, and future-proof framework for building and operating MCP servers.
For organizations looking to stay ahead in the agentic AI revolution, MCP AdaptiveOps offers a clear path forward—one that ensures reliability, flexibility, and compliance in an ever-changing ecosystem. By partnering with Mirantis, enterprises can confidently embrace the potential of agentic AI infrastructure while mitigating the risks associated with early-stage adoption.
To learn more about Mirantis MCP AdaptiveOps and how it can help your organization secure the future of Model Context Protocol, visit Mirantis’ website and read their latest blog post: Securing Model Context Protocol for Mass Enterprise Adoption.
About Mirantis
Mirantis delivers the fastest path to enterprise AI at scale, with full-stack AI infrastructure technology that removes GPU infrastructure complexity and streamlines operations across the AI lifecycle, from Metal-to-Model. Today, all infrastructure is AI infrastructure, and Mirantis provides the end-to-end automation, enterprise security and governance, and deep expertise in Kubernetes orchestration that organizations need to reduce time to market and efficiently scale cloud native, virtualized, and GPU-powered applications across any environment – on-premises, public cloud, hybrid or edge.
