AI agents are quickly evolving from experimental assistants to critical players in enterprise transformation. Across industries, they’re being tasked with navigating internal tools, coordinating workflows, and even making operational decisions, all in real time. But as their roles grow more complex, so too does the infrastructure required to support them.

Many existing enterprise systems were built around static APIs, pre-defined integrations, and task-based automations that are well-suited for traditional workflows, but not ideal for agents that operate dynamically and contextually. These systems often lack the responsiveness and flexibility needed for agents to interact securely and intelligently across diverse business functions.

To address this growing gap, the Model Context Protocol (MCP) has emerged as a promising standard. Championed by Anthropic and gaining rapid adoption across the AI ecosystem, MCP defines a consistent way for agents to interact with external tools, enabling structured usage, memory, and action loops. There are now thousands of MCP server implementations in open-source and enterprise contexts, and many major cloud providers are actively exploring hosted MCP capabilities as part of their evolving agent infrastructure strategies. The momentum toward standardized agent-tool interaction is accelerating fast.

However, most implementations focus narrowly on protocol adherence or developer convenience without addressing the broader operational needs of enterprise environments. As we noted in our previous article on building empowered AI agents, organizations need more than reactive automation, they need agents that can execute, coordinate, and adapt within real enterprise systems.

In this article, we explore how Rierino addresses that need by embedding MCP into its enterprise platform, not as a bolt-on protocol handler, but as an MCP middleware built for secure, orchestrated, and scalable agent execution. We’ll also examine how extending beyond the baseline MCP specification opens up new architectural possibilities, including low-code orchestration, distributed communication, and coordination across multiple MCP servers.

What Is MCP (Model Context Protocol) and Why It Matters for Enterprise AI Integration

The Model Context Protocol (MCP) was introduced as a way to help AI agents interact with external tools in a structured and consistent manner. Rather than hardcoding tool use into the model or relying solely on static API calls, MCP provides a communication protocol that enables agents to discover tools, request actions, receive streamed responses, and handle long-running tasks, all in a dynamic, context-aware loop.

MCP is based on a simple architecture involving three roles:

• The Client (protocol clients accessing servers through MCP),
• The Host (which mediates the session, often with embedded clients), and
• The Server (which exposes one or more tools the agent can invoke).

This model stands in contrast to conventional API-based integrations. While APIs are fixed and unidirectional, MCP is agent-native by design, enabling bidirectional, conversational interactions where the agent can explore available tools, reason through responses, and decide what to do next. It shifts integration from a one-off invocation model to a dynamic collaboration loop between agents and systems.

MCP has gained rapid momentum, not just in concept but in real experimentation. Beyond Anthropic’s implementation in Claude, developer communities have rapidly adopted MCP as a flexible way to connect agents with tools like databases, web services, and internal APIs. Cloud platforms such as Cloudflare have introduced hosted MCP capabilities with advanced features like session state and streaming support, and the open-source ecosystem continues to grow with experimental use cases ranging from browser automation to DevOps orchestration.

This breadth reflects a shared urgency: developers are eager to make agents useful beyond chat, and MCP offers a path forward.

Still, the majority of current implementations are designed with a developer sandbox mindset ideal for experimentation but not yet ready for enterprise complexity. Most MCP servers today:

• Focus on STDIO-based communication, limiting their usefulness to local or CLI-based toolchains
• Assume a single-agent/single-tool context rather than managing orchestrated flows or multi-step logic
• Lack support for common enterprise protocols like HTTP, WebSocket, or message queues, making integration with cloud-native infrastructure more difficult
• Offer limited observability, security, and fault tolerance, which are essential for real-world deployments at scale
• Provide little in the way of state management or session coordination, leaving it to developers to manually handle retries, error handling, or long-running tasks

These limitations aren’t flaws in the protocol, they’re signs that MCP is still early in its maturity curve, especially when applied to enterprise-scale problems. The question isn’t whether MCP is promising, it’s how to support it in a way that works in complex, secure, distributed environments.

That’s why enterprise integration of AI agents calls for more than protocol compliance. It requires an execution layer that understands the needs of both agents and enterprise systems — a platform where agents don’t just invoke tools, but participate in orchestrated, observable, and secure workflows. As we discussed in Designing Systems for AI as a User, enabling this level of interaction means thinking from the agent’s perspective and building systems that treat agents as users, not just API consumers.

Beyond Protocols: What Enterprises Really Need for Agent Execution

MCP provides a solid foundation for connecting AI agents to tools, but for enterprises, integration alone isn’t enough. A working protocol is a starting point, not the finish line. What matters is whether agents can actually operate within the constraints and complexities of enterprise systems.

That’s where the current landscape begins to show its limitations. Simply exposing a database query or a webhook through MCP might work in a development sandbox, but production workflows demand much more. Enterprise-grade agent execution depends on infrastructure that can handle:

• State management allowing agents to maintain context across multiple steps and interactions
• Flow orchestration coordinating actions across tools, systems, and services in structured, recoverable sequences
• Secure communication including encrypted transport, identity-aware access, and protocol governance
• Scalability and fault tolerance especially when agents initiate long-running tasks or operate across distributed environments

Agents don’t just need access to tools, they need a framework to reason through decisions, invoke coordinated actions, and recover gracefully when things go wrong.

This is where simply “wrapping” tools with an MCP interface falls short. It shifts the burden of complexity onto the agent or developer, expecting the agent to manage retries, session memory, sequencing logic, and error states, all without the support of a system that understands those patterns.

At Rierino, we take a different approach. Rather than treating MCP as an isolated protocol handler, we embed it directly as a middleware platform designed for agent execution. That means:

• Internal services and APIs can be made MCP-accessible without manual scaffolding.
• Agents can trigger multi-step workflows with built-in orchestration and rollback.
• Communication happens over protocols suited for real-world environments and not just local STDIO.

This vision moves beyond protocol support into the realm of agentic infrastructure — a foundation where agents can not only connect to tools, but also execute meaningful logic at scale.

How Rierino Embeds and Extends MCP for Enterprise Agent Execution

Rierino’s approach to MCP isn’t just about compatibility, it’s about making MCP usable, scalable, and production-ready inside enterprise environments. By embedding the protocol into a broader execution layer, Rierino turns what’s often a thin abstraction into something deeply functional — a programmable agent interface into your enterprise systems.

Let’s break down what this looks like in practice:

Built-In MCP Support

In Rierino, internal APIs and backend services can be made MCP-compatible without writing custom protocol wrappers or even manual mapping. The platform allows any configured flow, whether it pulls from a database, sends a notification, assigns task to a human, executes business rule or processes a transaction, to be exposed as a tool in a standard MCP schema.

Real-world developer scenario: A knowledge management team wants to enable an internal agent to retrieve up-to-date policy documents based on a department and topic. These documents live in a content management system (CMS), but the query requires combining data from multiple sources, including user permissions and document versioning.

• A flow is created to receive department and topic as inputs, check access rights from the identity service, and fetch the most recent document version from the CMS.
• This flow is exposed as an MCP tool called getPolicyDocument.
• The agent now sees it as a callable tool with a simple interface, but the backend handles permission checks, search logic, and fallbacks if no document is found.

Why it matters: This turns a multi-step enterprise task into a clean, secure interface for agents without creating brittle API endpoints or writing custom adapters. The complexity is managed inside the flow, while the agent experiences it as a single, smart capability.

Multi-Protocol Communication

Rierino supports multiple communication protocols beyond STDIO, including HTTP, WebSocket, and MQ. This enables agents to interact with backend systems across a variety of transport methods which is critical for modern, distributed, and event-driven architectures.

Real-world developer scenario: A customer support agent needs real-time updates from the logistics system while waiting for background tasks like invoice generation to complete asynchronously.

• The agent calls a tool trackShipmentStatus, which is configured to receive live updates via WebSocket from a shipping API.
• Simultaneously, a task is dispatched to generate an invoice using a message queue (Kafka) that triggers a flow downstream.
• The agent receives an initial MCP response with status info, and a follow-up message once the invoice is generated and available.

Why it matters: Rierino supports rich, asynchronous workflows where agents don’t have to block or poll for updates. This makes it possible to integrate AI agents into real-time and decoupled system landscapes without workarounds.

Orchestration and Flow Execution

At the core of Rierino is a low-code saga flow designer that lets developers model complex, stateful workflows. These flows can include retries, conditional branching, compensating actions, and parallel processing, which are all essential for robust execution.

Real-world developer scenario: An HR agent is asked to initiate a new employee onboarding process that spans multiple systems.

• The flow behind the tool startEmployeeOnboarding provisions accounts in the identity provider, sets up payroll in the finance system, and schedules compliance training.
• If the payroll system returns an error, the flow compensates by rolling back the account setup and notifying HR.
• The agent only sees the high-level tool invocation, but the underlying flow handles every step and rollback condition.

Why it matters: This elevates agent capabilities from simple task invocation to enterprise-grade orchestration, where agents trigger full business processes with resilience and accountability.

Cross-MCP Coordination

Rierino supports calling and coordinating with other MCP servers directly within flows, making it a natural choice for organizations adopting federated agentic architectures.

Real-world developer scenario: A procurement agent is tasked with preparing a bundled offer that pulls data from three systems managed by different teams.

• The tool createCustomOffer is backed by a flow that:
  • Pulls specs from a product configuration MCP server,
  • Retrieves discounting rules from a pricing MCP server,
  • Submits the proposal via a CRM MCP server.
• Each server responds independently, while Rierino's flow handles aggregation, error handling, and timeouts.

Why it matters: Rather than centralizing every tool into one server, Rierino enables coordination across decentralized MCP domains, reducing duplication and making it easier for large organizations to scale AI agent workflows incrementally.

Security, Observability, and Control

Rierino includes built-in mechanisms for governing agent activity from session-level observability to detailed access control. This ensures that agents operate within defined boundaries and that every action is traceable.

Real-world developer scenario: A finance agent modifies a vendor’s payment terms, triggering an alert from the compliance team.

• The tool updateVendorPaymentTerms is governed by role-based access controls (RBAC), ensuring only agents with proper scopes can invoke it.
• The compliance team opens the session log and reviews the inputs, flow path, approval conditions, and downstream impacts.
• A session replay shows exactly when and why the approval logic permitted the change — down to the tool response payloads and compensating steps.

Why it matters: AI agents in the enterprise must be observable, auditable, and defensible. Rierino doesn’t just give agents power, it gives organizations control over how that power is used.

Rethinking the Role of Middleware in the Age of AI

As AI agents evolve from productivity assistants to autonomous collaborators, the traditional role of middleware is being redefined. It’s no longer enough for middleware to simply shuttle data between systems. In the age of agentic software, middleware must also support orchestration, reasoning, and secure execution — the kinds of capabilities agents now require to operate responsibly within enterprise environments.

This shift has led to the emergence of architectural patterns like MCP-as-a-Service (MCPaaS) — not necessarily a product or hosted offering, but a design principle. The idea is simple: AI agents should be able to access enterprise tools through a structured, protocol-compliant interface that abstracts away integration complexity while respecting enterprise constraints. Rierino embodies this approach by embedding MCP within a platform that already supports execution, state handling, and secure coordination.

Rather than bolting on an MCP server to expose a few tools, enterprises using Rierino can elevate middleware into something more powerful: a control plane for AI agents. With built-in flows, multi-protocol support, and system-aware orchestration, the platform doesn’t just connect systems, it enables autonomous logic to run across them safely and at scale.

This rethinking is especially important in complex domains like ecommerce. In Agentic Commerce: Driving Ecommerce with AI Agents, we explored how agents can drive workflows across PIM, OMS, and CMS platforms, but only when the middleware enables those systems to be orchestrated, not merely accessed. Agent-driven experiences are possible not because the agent has access to every tool, but because the execution logic between tools is coordinated, monitored, and governed.

The broader implication is clear: as agents become more capable, the middleware beneath them must become more intelligent. It's not about protocol compatibility, it's about execution compatibility. Middleware must understand context, support flow-based thinking, and provide the hooks for visibility, security, and resilience.

Building the Future of Agentic Infrastructure

The rise of AI agents is reshaping how enterprise systems are designed, integrated, and operated. As these agents move from isolated assistants to autonomous decision-makers, they require more than just access to APIs or tools, they need infrastructure that supports context, coordination, and control.

The Model Context Protocol (MCP) has emerged as a critical foundation in this shift, offering a structured interface for agent-tool communication. But as we've seen, protocol support alone isn't enough. The future of MCP, and of agentic platforms in general, lies in how protocols are grounded in systems that support execution, not just invocation.

That’s why Rierino integrates MCP as part of a broader enterprise platform — one that treats agent requests as the entry point to robust, orchestrated, and observable workflows. MCP is not an add-on, but a first-class interface into business logic already designed to scale, recover, and comply.

By extending the reach of MCP across services, protocols, and systems, while embedding it into the tools enterprises already use, we intend to lay the foundation for AI agents that do more than assist. They act with structure, respond with context, and operate within guardrails that make them reliable collaborators across the enterprise.

Want to start building with MCP in a real enterprise environment? Explore Rierino’s platform to connect your agents, orchestrate flows, and experience what execution-ready middleware truly looks like.