The News
Mirantis announced the launch of MCP AdaptiveOps Services, a new set of consulting and engineering offerings designed to help enterprises design, build, and operate Model Context Protocol (MCP)–based agentic infrastructure. Building on its MCP AdaptiveOps framework introduced earlier in 2025, Mirantis aims to help teams move MCP from experimentation into production while navigating a rapidly evolving standards and tooling ecosystem.
Analysis
MCP Moves from Experimentation Toward Enterprise Operations
The Mirantis announcement reflects a broader inflection point that we have been tracking: MCP is shifting from early experimentation into an operational concern for platform and application teams. As MCP governance transitions into the open-source community, adoption is accelerating, but so is uncertainty. Developers face a fragmented landscape of registries, gateways, LLM routers, and emerging best practices, all evolving faster than most enterprises can safely absorb.
Our application development research shows that while interest in agentic AI is high, production readiness remains low. Less than half of organizations experimenting with AI agents report having a clear operational model for governance, observability, and lifecycle management. Mirantis is positioning MCP AdaptiveOps as a stabilizing layer, one that helps enterprises build around open standards and flexible architectures, rather than betting prematurely on any single implementation detail.
This signals that MCP is no longer just a developer curiosity. It is becoming an infrastructure concern that requires platform-level thinking.
What MCP AdaptiveOps Services Mean for Application and Platform Teams
Mirantis’ services portfolio targets a critical gap: many teams understand why they want agentic systems but lack the operational scaffolding to build them responsibly. The services span the full maturity curve, from short readiness assessments to multi-month platform design and implementation efforts.
For developers, this matters because MCP-based systems introduce new complexity that traditional DevOps practices were not designed to handle. MCP servers sit at the intersection of AI models, tools, APIs, identity, and governance. Building them ad hoc often results in brittle architectures that are difficult to secure, audit, or evolve.
By focusing on reusable MCP server templates, standardized workflows, and production-ready operating models, Mirantis is emphasizing repeatability over one-off builds. That approach aligns with how platform engineering teams increasingly think about AI infrastructure: not as a single application, but as a shared internal service that multiple teams will consume.
The inclusion of services around multi-tenancy, observability, and LLM integration suggests Mirantis is treating agentic platforms as first-class infrastructure, similar to Kubernetes platforms in earlier cloud-native waves.
Agentic AI Lacks a Proven Operating Model
Across enterprise environments, the biggest barrier to agentic AI adoption is not model capability; it is operational risk. We consistently see concerns around identity, policy enforcement, auditability, and long-term maintainability slow or stall agentic initiatives.
MCP introduces promise through standardization, but standards alone do not solve enterprise realities. Teams still need guidance on how to manage versioning, enforce access controls, observe agent behavior, and align AI systems with internal risk frameworks. Mirantis’ inclusion of an AI Risk & Compliance Operating Model offering acknowledges that agentic systems will be held to the same scrutiny as other mission-critical platforms.
This mirrors patterns we’ve seen before. Kubernetes adoption only accelerated once enterprises had reliable operating models, reference architectures, and ecosystem expertise. MCP appears to be entering a similar phase where success depends less on raw innovation and more on disciplined execution.
Developer Behavior Going Forward
As MCP matures, developers may begin shifting from building bespoke agent integrations toward consuming standardized MCP services provided by internal platforms. That transition would allow application teams to focus on agent logic and user value, while platform teams manage governance, observability, and lifecycle concerns.
Mirantis’ AdaptiveOps services may encourage organizations to treat MCP infrastructure as a shared foundation rather than an experimental side project. Developers could see clearer guardrails around how agents interact with tools, data, and models, thus reducing the risk of rework as standards evolve. While results will vary by organization, this approach may shorten the path from proof-of-concept to production by giving teams a clearer operational blueprint.
Looking Ahead
The agentic AI ecosystem is still early, but it is maturing quickly. As MCP adoption accelerates under open-source governance, enterprises will need operating models that can absorb change without constant re-architecture. The next phase of adoption will favor teams that build around open standards, modular designs, and platform-level abstractions.
Mirantis’ expansion of MCP AdaptiveOps into formal services positions the company as an enabler of this transition, from experimental MCP deployments to sustainable, enterprise-grade agentic platforms. Whether MCP follows the same trajectory as Kubernetes will depend on how well the ecosystem converges on shared practices. What is clear is that agentic AI is becoming an operational problem, and enterprises are beginning to seek partners that can help them navigate that shift with discipline rather than hype.
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