MCP introduces a smarter, more modular way for AI systems to work together. At Claris AI, we’re actively architecting with these principles in mind.

AI systems are evolving fast. But the way they connect, or often fail to connect, remains a major challenge.

Many enterprise AI tools today operate in silos. A model might generate content but has no awareness of what happened two steps earlier. A search tool might return accurate results, but those results don’t carry forward into downstream tasks. Connecting these parts often requires layers of custom logic.

That’s where Model Context Protocol, or MCP, comes into the picture.

Introduced by Anthropic, MCP is an open standard that allows different AI components — models, tools, agents, memory systems — to share context and operate as a unified system. Instead of wiring everything together manually, MCP provides a framework for modular, secure collaboration between intelligent parts.

At Claris AI, we’ve been building our architecture to support this kind of interoperability. Our engineering team is actively evaluating MCP-aligned approaches and open-source implementations to integrate into our core AI platform.

What Problem Is MCP Trying to Solve?

Most AI deployments today are stitched together one use case at a time. They work—until they don’t. Add a new model, update a tool, or change a data source, and suddenly everything needs reconfiguring.

MCP solves this by creating a shared layer of context that can be accessed and updated across your entire AI system. It allows tools and models to understand the bigger picture, work together dynamically, and remember what’s already been done without constantly reinventing the wheel.

That kind of structure is essential for scale. It also reduces risk, especially when compliance and governance are critical.

What MCP Looks Like in Plain English

Right now, many AI tools are like appliances in a kitchen. They do their job, but they don’t talk to each other. The espresso machine doesn’t know what the oven is doing, and the dishwasher has no idea what came before.

MCP is like a smart kitchen control system. It keeps everything connected, aware, and coordinated. Each component understands the context of what’s happening and can respond accordingly.

For enterprise AI, this means systems that chain together seamlessly, hand off tasks, adapt in real time, and maintain a clear trail of decisions and actions. It works smoothly no matter how many tools or teams are involved.

How Claris AI Is Approaching This

At Claris, we’ve built our platform on a modular foundation, designed to support distributed systems, multiple models, and agent-based workflows.

Our current development roadmap includes integrating open, MCP-compatible infrastructure into our core AI system, allowing agents to maintain shared memory, call tools with full awareness of task context, and enable tenant-level customization with future support for connecting third-party MCP servers.

This direction aligns with how we’ve always built: systems that are transparent, adaptable, and secure from the ground up.

Why This Matters for Regulated Industries

In industries like pharma, finance, and manufacturing, AI adoption is never just about speed or automation. It’s about control — control over how systems make decisions, how context is shared, who can access what, and whether every step can be explained and trusted.

Model Context Protocol (MCP) offers a framework that directly supports these needs. It introduces a structured way for different parts of an AI system to collaborate while preserving clarity, traceability, and modularity. This is especially valuable in high-stakes environments where compliance, oversight, and auditability are non-negotiable.

At Claris AI, we’re actively building with these principles in mind:

• Shared context ensures that models, tools, and agents operate with access to consistent, up-to-date information across workflows

• Agents orchestrate intelligent task routing, connecting the right models and tools based on real-time needs

• Memory systems retain key history and task evolution, allowing continuity across sessions and improving outcomes over time

• Tool execution is securely handled with context awareness, enabling our AI systems to activate internal APIs or external functions without losing track of the bigger picture

• Model flexibility means we can adapt or swap LLMs to fit specific enterprise use cases or compliance requirements

• Full audit trails capture decision-making processes and outputs, providing transparency for both internal teams and regulatory reviews

• Composable architecture lets enterprises update, scale, or reconfigure their systems with minimal disruption

This architecture supports everything from SOP validation in life sciences to intelligent customer service in manufacturing and adaptive knowledge systems in finance.

By designing for interoperability, traceability, and modularity, we help our clients build AI that’s not only powerful — but also accountable, adaptable, and ready for the demands of regulated environments.

What’s Next

As MCP standards and implementations continue to mature, we’re already designing for compatibility. We’re evaluating, experimenting, and building toward full integration within our platform.

Whether through direct adoption or aligned infrastructure, the direction is clear. Modular, compliant AI is what the real world needs.

If you’re leading transformation in a space where AI must be explainable, traceable, and resilient, we’d love to connect. Because trust isn’t a feature. It’s a foundation.