MCP has quickly become the default way teams connect AI to business systems. Every week brings new announcements: DIY MCP servers, MCP-compliant APIs, vendors shipping “MCP integrations,” and developers wiring models to APIs in hours. MCP is having its breakout moment.

But here is the real issue. The market is treating all MCP implementations as equivalent, even though they sit at very different levels of maturity and ambition. Most MCPs today provide a connection, not a foundation. They let an AI call a function, but they stop short of providing the architecture required for AI to operate safely, predictably, and at enterprise scale.

As a result, organizations are not just taking on unnecessary risk—they are dramatically undershooting what AI can actually deliver.

This gap is already visible in the data: Gartner warns that over 40 percent of AI projects will be cancelled by 2027 because they lack the right governance and architectural foundation. McKinsey notes that companies struggle to scale AI because they lack trusted access, shared context, and orchestration across systems.

But the deeper problem is not that AI projects fail. It’s that they plateau.

Teams connect models to tools, deploy copilots, and automate isolated tasks—then stop. Without an enterprise-grade foundation, AI remains confined to surface-level productivity gains instead of becoming a driver of end-to-end process transformation. Organizations get faster answers, but not fundamentally better operations.

Three distinct MCP models have emerged to support these efforts: DIY MCP, Application MCP, and Enterprise MCP. They all use the same name, but they are built on fundamentally different assumptions and enable very different outcomes.

Only one of them is designed for AI that must operate inside core systems, respect enterprise policies, coordinate across processes, and deliver measurable business results.

1. DIY MCP: Maximum Flexibility, Minimum Safety

DIY MCP is what most technical teams start with. It includes open source libraries, custom servers, LangChain glue code, or lightweight agent frameworks that let developers wire models directly to APIs. It feels fast, flexible, and empowering, especially during experimentation.

Teams choose this approach because they can move quickly, test ideas, and build proof of concepts without waiting for vendor roadmaps. It works well for hackathons and sandbox projects.

The problems appear the moment teams try to move from prototype to production.

DIY MCP has no real governance. There is no identity inheritance, no audit trail, no traffic control, and no protection against hallucinated actions. Each agent becomes a one-off implementation that only its original developer understands. Maintenance costs rise, trust declines, and the approach fails under real workloads. McKinsey calls this the “skill gap and context gap” that stops agents from scaling beyond prototypes.

DIY MCP leads to agent sprawl, shadow AI infrastructure, and inconsistent behavior across environments. The security risk climbs quickly as developers deploy servers on laptops, unmanaged cloud instances, or personal environments with no visibility for IT or the CISO. There is no reuse of business processes, no enterprise skills, and no way to ensure safe execution inside real workflows.

DIY MCP is valuable for experimentation but not sustainable for production.

2. Application MCP: Valuable, but Inherently Limited

The second category is Application MCP. This is when a software vendor exposes a small MCP surface for only their application. Examples include Jira MCP or ServiceNow MCP. These implementations usually wrap existing APIs and provide just enough structure to power that vendor’s own copilot.

Application MCP works well inside a single ecosystem. It makes vendor copilots smarter and easier to use. Many are bundled for free with the application. For teams that need quick productivity boosts within one system, this model is useful.

The limitations are significant. Application MCP only exposes what the vendor controls, which means there is no shared context across applications. There is no multi-step process logic, no orchestration, and no enterprise level identity or policy enforcement. Each vendor implements MCP differently, which increases fragmentation rather than reducing it.

As our CEO, Vijay Tella, noted in the Workato WoW 2025 Keynote, this is not enterprise orchestration. It’s some duct tape compared to a full toolset. The agent can only do what the vendor exposes. It cannot add new enterprise skills, understand multi-system processes, support KPI-driven work, or collaborate with other agents.

Gartner predicts that by 2026, more than 40 percent of enterprise applications will include task specific agents, which will create a new wave of agent silos unless organizations add an orchestration layer. Application MCP is one contributor to that fragmentation.

This is what Application MCP leads to. Dozens of disconnected vendor agents, each operating in its own silo. No enterprise knowledge graph. Inconsistent governance and identity. Surface level workflows that break the moment a process crosses systems.

So when do I need an ‘agent’ vs MCP?

You often want the vendor’s copilot, but you still need an enterprise layer to connect, manage, and govern everything. Application MCP servers don’t remove the need for Enterprise MCP—they often increase it.

When to Buy an App Agent	The work stays entirely inside a single application. You are optimizing individual productivity, not system behavior. Approximate or suggestive answers are acceptable. AI is assisting humans, not executing business actions. The use case is summarization, drafting, or navigation.
When App MCP Is Enough	AI needs to read or update a limited set of records in one system. Responses must be grounded in real data, but only within that app. The operation is low risk and easily reversible. The application itself defines permissions and guardrails.
When You Need Enterprise MCP	AI must retrieve or act on authoritative enterprise data. Hallucinations, guesses, or partial answers are unacceptable. Requests must respect real user identity and permissions. Actions must follow approved, deterministic business logic. All activity must be fully auditable and policy controlled. Any AI model must behave consistently and safely.

App MCP gives you tools. Enterprise MCP gives you trust, context, skills, and orchestration.

App-level MCP improves accuracy by letting AI fetch data or trigger actions inside a single system. But it still assumes the AI can reason correctly, choose the right operation, and stay within invisible boundaries. That’s acceptable for low-risk use cases.

Enterprise MCP exists for the moments where AI cannot guess. When AI is asked to look up a customer, validate an account, issue a refund, provision access, or touch systems of record, the problem is not intelligence—it’s infrastructure.

Enterprise MCP introduces a governed execution layer that constrains what AI can see, what it can do, and how it does it. It replaces fragile prompts with deterministic skills, enforces identity and policy at runtime, and ensures every action is traceable.

This is what allows any AI model (Claude, GPT, Gemini, etc.) to operate safely inside the enterprise without becoming a source of risk.

3. Enterprise MCP: What It Takes to Run AI in Production

Enterprise MCP, like Workato’s, is fundamentally different. It is not a thin API wrapper. It is a complete layer for trust, context, skills, and orchestration. It is built for enterprise workflows, not prototypes or app specific assistants.

Enterprise MCP includes enterprise context, knowledge graphs, skills, identity inheritance through AgentAuth, governance, observability, resilience, process understanding, and KPI driven design. It connects to more than ten thousand applications and systems and provides enterprise skills that allow AI to take predictable actions safely.

This approach works at scale because it aligns with what Enterprise AI needs.

What AI Needs	What Workato’s Enterprise MCP Provides	Why It Matters
Orchestrated context	Unified data, process awareness, and real-time signals	AI understands the full situation instead of acting in isolation
Trust and security	Access control, audit logs, policy enforcement, identity inheritance	Every action is traceable, governed, and safe for core systems
Accuracy	Enterprise skills that execute predictable, complete business actions	Removes ambiguity and ensures correct outcomes every time

Enterprise MCP delivers multi-step orchestration, resilience, real-time monitoring, process understanding, and full observability. It works with any LLM and orchestrates any third party agent. With Enterprise MCP, AI becomes KPI driven, explainable, supervised, resilient, and coordinated. They do not just chat. They do work.

Comparison of All Three Approaches

The differences between DIY MCP, Application MCP, and Enterprise MCP become most obvious when you compare them across the capabilities that matter most for running AI in production. Each approach offers a different level of trust, context, scalability, and operational readiness. This side-by-side view highlights where each tier performs well and where it breaks down.

Dimension	DIY MCP	App MCP	Enterprise MCP
Time-to-Value	✅ Fast to prototype	✅ Fast in one app	✅ Fast to production at scale
Upfront Cost	✅ Low platform cost (high dev)	⚠️ Included or add-on	✅ Lower TCO via consolidation
Flexibility (Builder Freedom)	✅ Maximum flexibility	⚠️ Bound to vendor surface	✅ Flexible, but governed
Security and Identity	🚫 No enterprise identity inheritance by default	⚠️ Vendor-scoped identity	✅ Full enterprise identity with AgentAuth
Scoped Permissions	🚫 Hard to enforce consistently across systems	⚠️ Scoped only inside the app	✅ Fine-grained, runtime-scoped access
Governance and Observability	🚫 No centralized governance plane	⚠️ App-only visibility	✅ Centralized enterprise governance
Compliance and Auditability	⚠️ Possible, but bespoke and fragmented	⚠️ App-level compliance	✅ Enterprise-grade compliance and traceability
Enterprise Context	🚫 Not realistic to sustain manually across systems	⚠️ Strong context in one system	✅ Unified data + process context + real-time signals
Enterprise Skills	⚠️ You can build tools, but you own quality & drift	⚠️ Vendor-limited	✅ 900,000+ governed enterprise skills
Accuracy and Reliability	🚫 No durable reliability layer (retries, rollback, exactly-once)	⚠️ Reliable within the app only	✅ Orchestration-grade execution (durable + predictable)
Error Handling and Recovery	🚫 Breaks become engineering incidents	⚠️ Vendor-dependent handling	✅ Built-in controls: retries, approvals, replay
End-to-End Orchestration	⚠️ Possible, but becomes custom orchestration	🚫 Single-app boundary	✅ Cross-system business processes
Operational Scalability	⚠️ You operate and scale everything	⚠️ Vendor-constrained	✅ Elastic, resilient, serverless
AI Model Independence	✅ Full control	⚠️ Vendor-locked	✅ Works with any LLM or AI system
Business Outcomes	⚠️ Great for pilots; hard to standardize	⚠️ Local productivity gains	✅ KPI-driven outcomes across functions

Why This Matters Now

The MCP conversation often focuses on agents, but the reality is broader. Whether an organization is deploying a company-wide Claude rollout, enabling copilots inside core applications, or experimenting with autonomous agents, the same question applies:

Can this AI safely interact with enterprise systems?

The moment AI is allowed to look up customers, read financial data, provision access, update records, or trigger workflows, intelligence is no longer the bottleneck—enterprise infrastructure is.

Enterprise MCP exists to solve that problem. It provides a governed execution layer between AI and the enterprise—one that enforces identity, permissions, policy, and process determinism regardless of which model is used or how advanced the AI appears. It ensures AI operates with real context, predictable actions, and full traceability.

This is why Enterprise MCP is relevant even before organizations deploy agents. Copilots, chat interfaces, and AI-powered workflows all face the same risks once they move beyond suggestions and into action. Without enterprise-grade identity, skills, orchestration, and observability, AI remains confined to low-risk productivity use cases.

DIY MCP enables experimentation. Application MCP improves single-app experiences. Enterprise MCP is what allows AI—of any kind—to participate in real business processes with confidence.

Workato Enterprise MCP is built for this reality. It unifies connectivity, context, skills, and governance in a single platform, enabling AI systems to execute reliably across applications and environments. It turns AI from a promising interface into a dependable operational capability.

The organizations that succeed in the next phase of AI adoption will not be the ones that connect models the fastest. They will be the ones that build the right foundation—so AI can act safely, scale confidently, and deliver measurable outcomes.

Enterprise MCP is that foundation.