Making MCP Enterprise-Ready: Why Protocol Alone Isn’t Enough

Christopher Hedley
Oct 27
3 min read

Updated: Oct 29

AI is everywhere — at least in slide decks. But for platform leaders, the gap between hype and reality is enormous. Many are told to “do something with AI” while still maintaining control over cost, security, and compliance.

Despite $30–40 billion already invested in GenAI, MIT NANDA reports that 95% of initiatives fail to show measurable business impact. AI has arrived, but enterprise value hasn’t.

That’s where the Model Context Protocol (MCP) fits. And it’s Syntasso Kratix Enterprise that turns MCP from an unrealised opportunity into a provided-by-default reality.

MCP is more than another API wrapper

Most “AI integrations” today are little more than wrappers around a handful of global models. They demo well but rarely deliver repeatable business value. The Model Context Protocol (MCP) differs: it standardises how automation tools, including AI agents, interact with systems in a structured, contextual, and governed manner. This enables workflow-aware APIs, not just model calls.

But the protocol alone doesn’t solve the real problems. Enterprises struggle because AI tools fail to learn, adapt, and integrate into workflows. That is the true barrier. According to MIT NANDA research, the top reasons GenAI tools stall are resistance to static analysis tools, poor integration, and a lack of memory or learning capabilities.

This is where Kratix and Syntasso Kratix Enterprise (SKE) come in. Kratix doesn’t just expose interfaces; it enforces contracts, tracks usage, applies policies, and turns internal automation into discoverable platform APIs ready for use by humans or AI agents alike.

Platform Promises: From automation to safe services

At the heart of Kratix are Promises. These are platform-defined contracts that describe how infrastructure should be delivered, governed, and accessed. Kratix Enterprise automatically converts these into MCP endpoints, eliminating the need for you to design new APIs or build new logic.

The result is a clean boundary where platform teams define the rules, and developers and AI agents consume services through interfaces that apply those rules by default.

This is precisely the kind of structure that the State of AI in Business 2025 report identifies as missing in most failed implementations. Only 5% of custom enterprise AI tools make it to production, often because they break in edge cases or fail to match operational realities. By contrast, the few that succeed are described as workflow-aware and learning-capable, which is the exact model Kratix was designed to support.

This isn’t just theory; we built a prototype

In a recent prototype, we connected a Claude-based AI assistant to MCP endpoints exposed by Kratix. The assistant could discover available services, request a Redis instance, check valid clusters, and update that instance — all without documentation, YAML, or kubectl.

It worked because the logic was already in the platform. The AI was not fabricating information. It was interacting with existing automation, scoped by contracts and surfaced by Syntasso Kratix Enterprise.

Focus on enterprise readiness, not AI hype

Kratix Enterprise doesn’t assume AI is reliable. It makes it manageable. Platform teams define the capabilities that are exposed and where they are located. Policies apply regardless of whether the trigger is a CLI, portal, or AI agent.

This is not a bolt-on AI wrapper. It is a platform product designed to keep control in the hands of those accountable for security, cost, and compliance.

And it fits with existing tools. You can elevate your existing Terraform, Helm, or pipelines into Promises. Kratix wraps them, exposes them through MCP, and lets agents interact safely with zero access to internal logic.

The State of AI in Business 2025 makes a strong case for this model. Purely internal builds were found to fail twice as often as external partnerships, and the most successful deployments focused not on big front-office demos but on high-ROI internal processes. But blending purchased solutions with internal control through a framework for managing AI is the sweet spot.

Move fast on your terms (human or AI)

Enterprises face pressure to deliver faster, adopt AI, and reduce risk. Most tools make you choose two out of three.

Syntasso Kratix Enterprise (SKE), with native MCP support, provides the structure to accomplish all three. It turns automation into a product. It provides interfaces that you can govern. And it helps your platform participate in the next generation of intelligent workflows.

You don’t need to chase AI hype. What enterprises need is automation that’s safe, governed, and ready for both humans and AI agents.

That’s what Kratix delivers — and that’s what makes MCP more than a protocol.

👉 Ready to see how it works?