Model Context Protocols (MCPs) standardize how AI agents securely access enterprise systems in real time. They allow agents to retrieve context, interact with internal tools, and take action without relying on brittle, custom-built integrations that are difficult to scale.
That capability informs the value of AI, which depends on access to trusted, governed business data. The imperative of this capability becomes even more pronounced as organizations shift from experimental to production use.
As businesses deploy AI agents across core workflows, they need MCP platforms designed for production use. In response, leading platforms are converging around a common set of capabilities that provide a useful lens for those evaluating MCP platforms in the market.
Production-Ready MCP Platforms
Recently, CData published a guide for enterprise IT leaders outlining MCP platform features organizations are likely to require as adoption accelerates. The framework offers a structured way to assess whether emerging MCP platforms are prepared for production.
Accordingly, CData defines the following 10 MCP features as essential in 2026:
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Plug-and-play integrations
Prebuilt connectors that allow AI agents to connect to enterprise systems with minimal configuration and without custom development.
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Real-time monitoring
Continuous tracking of AI process status, context access logging, API call monitoring, and audit trail creation, supported by centralized execution and logging.
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Scalability
The ability to support growth in agents, users, and data sources through architectures that scale components independently as demand increases.
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Security and compliance controls
Support for modern authentication, role-based access, encrypted data flows, execution boundaries, and alignment with enterprise compliance standards.
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User-friendly interfaces
Point-and-click, drag-and-drop designs that enable non-developers to create and manage connections without coding, supporting adoption and self-service.
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Automated deployment and lifecycle management
Support for continuous integration and delivery, including automated validation, testing, and updates, without requiring customers to manage infrastructure.
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Error handling and validation
Mechanisms to detect, manage, and recover from failures through validation, structured logging, and controlled degradation.
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Cross-platform agent compatibility
The ability to support multiple AI agents and environments through a consistent interface, reducing dependency on a single vendor or toolchain.
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Support for AI-driven automation
Capabilities that enable agents to query systems, process documents, orchestrate workflows, and interact with applications.
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Flexible commercial models
Pricing structures that align cost with usage patterns and scale over time, supporting predictable budgeting as adoption expands.
Choosing an MCP Platform
When assessing an MCP platform’s individual features, leadership teams need to consider how those capabilities work together under production conditions.
For example, platforms that emphasize integration breadth but lack governance controls could introduce risk. Others may offer strong security, but require extensive customization, limiting scalability as adoption grows under production.
The practical challenge for buyers lies in evaluating trade-offs. Buyers should assess whether a platform supports secure, real-time system access, scales as agent usage grows, and maintains control as AI becomes embedded in core operations. Governance, observability, and lifecycle management become as important as connectivity.
Over time, these factors determine whether AI agents remain confined to pilots or become dependable components of enterprise workflows. MCP platforms that balance integration depth with operational discipline are best positioned to support long-term adoption.
What This Means for ERP Insiders
AI agents are becoming ERP actors. MCP platforms determine whether AI can safely operate inside ERP systems as trusted users. This influences automation scope, control models, and confidence in AI-driven decisions. The quality of that integration shapes whether ERP automation scales reliably or remains constrained.
ERP risks increasingly sit in integration layers. As AI agents access ERP data, MCP platform capabilities shape compliance posture, audit readiness, and operational resilience. Weak integration controls can turn otherwise stable ERP systems into sources of operational and regulatory risk.
ERP modernization is shifting from customization to orchestration. Production-ready MCPs reduce reliance on bespoke ERP extensions, allowing CIOs to scale AI capabilities through standardized platforms. This shift lowers upgrade friction and makes it easier to introduce new AI-driven processes without destabilizing core ERP systems.
