Despite massive investment, most AI initiatives still stall at the pilot stage—impressive demos that never quite graduate into systems that can take real action. Workato believes it knows why, and it’s betting big on the fix.
The company announced the launch of production-ready Model Context Protocol (MCP) servers, positioning itself as the first vendor to deliver enterprise-grade infrastructure built specifically to operationalize MCP at scale. The initial release includes eight MCP servers spanning communications, productivity, sales, and IT operations, with more than 100 servers planned for release this year.
The move targets a problem that has quietly become one of the biggest blockers to enterprise AI: AI agents can reason, but they can’t reliably act inside real business systems.
The MCP Promise—and the Gap That Followed
When Anthropic introduced MCP in 2024, it did something important: it defined a standard protocol for connecting AI agents to business tools and data. That standard quickly gained traction across the industry.
But standards don’t run themselves.
Enterprises soon discovered that while MCP explained how AI could connect to systems, it didn’t solve the much harder problem of running those connections in production. Security models were inconsistent. Governance was fragmented. Reliability and auditing were often afterthoughts.
In other words, MCP existed—but enterprise-grade MCP infrastructure didn’t.
That gap has proven expensive. Companies have been forced to choose between building and maintaining custom MCP servers internally—burning engineering time and budget—or waiting for individual SaaS vendors to roll out partial, incompatible solutions.
Neither option scaled.
Why “Production-Ready” Actually Matters
Workato is drawing a sharp line between experimental MCP implementations and what it calls production-ready MCP servers.
In this context, production-ready isn’t marketing fluff. It means:
- Enterprise-grade security
- Granular role-based access control
- Comprehensive audit logging
- High availability with 99.9% uptime
- Professional support and lifecycle management
Without those foundations, AI agents may be able to retrieve data—but enterprises can’t trust them to operate safely inside systems of record.
That trust gap is what keeps AI stuck summarizing emails instead of executing workflows.
Infrastructure, Not a Patchwork
One of Workato’s central arguments is that point solutions can’t solve an infrastructure problem.
Individual SaaS vendors building MCP endpoints create three systemic issues:
- Inconsistent security and identity models
- No way to compose workflows across systems
- Fragmented governance and auditing
Enterprises don’t want dozens of disconnected MCP implementations. They want a unified, governed layer that works across tools and teams.
This is where Workato believes it has a structural advantage. The company isn’t starting from scratch—it has already been running enterprise automation and AI workloads in production for thousands of customers. MCP servers simply expose that same hardened infrastructure through an industry-standard protocol.
The First Eight MCP Servers
Workato’s initial rollout includes eight pre-built MCP servers designed to cover common, high-value enterprise workflows:
- Google Calendar – Scheduling, availability checks, focus time, and event management
- Google Sheets – Read data, append rows, and update cells atomically
- Google Directory – People discovery, profiles, and org context
- GitHub – Repositories, issues, pull requests, code search, and commit history
- Gong – Call history, transcripts, and meeting context
- Slack – Search conversations, retrieve history, post messages, and manage canvases
- Jira – Search, create, and update issues via natural language
- Okta – Identity resolution, group membership, and access context
These servers can be deployed in minutes. Workato hosts and maintains the infrastructure, removing the need for customers to manage keys, scaling, uptime, or compliance.
Just as important: the servers work together. An AI agent can retrieve customer context, schedule meetings, draft communications, and update systems of record—without breaking governance or permissions.
From Agents That Talk to Agents That Work
The deeper implication here is about agentic AI.
Most AI agents today are conversationally capable but operationally constrained. They can suggest actions, but humans still have to execute them. That’s not because the models lack intelligence—it’s because they lack secure, governed access to enterprise systems.
Workato’s MCP servers are designed to change that.
By combining MCP servers with its existing orchestration capabilities, organizations can turn business processes into callable tools for AI agents—without custom code. Any “skill” built in Workato can be exposed as an MCP tool, immediately usable by agents like Claude, ChatGPT, and Cursor.
That’s how AI moves from assistant to operator—while staying within enterprise guardrails.
Why Workato Is Leaning In Now
Workato introduced Enterprise MCP in October as a broader platform for connecting AI agents to enterprise data and workflows. MCP servers are the missing execution layer.
“What holds back AI in the enterprise is the ability to get to business data and drive action,” said CTO Adam Seligman. “Enterprises need MCP hardened with the security and governance they require. That’s what Workato Enterprise MCP delivers.”
The company plans to release over 100 pre-built MCP servers this year, with priorities driven by an open, customer-led roadmap—a notable contrast to vendors quietly picking integrations behind closed doors.
Customer Perspective: Lowering the Barrier to Agentic AI
Early enterprise feedback suggests MCP is already reshaping how teams think about AI deployment.
“MCP lowers the barrier,” said Kevin Wolf, Senior Director of IT Operations at Swanson Health. “It allows AI to talk and adapt directly with APIs and underlying systems. Workato’s MCP gives agentic AI a broader, governed awareness of what data it can access and how to use it.”
That governance angle matters. As regulators, security teams, and auditors scrutinize AI behavior, enterprises need to prove not just what an agent did—but why it was allowed to do it.
The Bigger Picture: AI Needs Plumbing
Workato’s announcement underscores a broader industry shift. The next phase of enterprise AI isn’t about smarter models—it’s about better plumbing.
AI can’t transform operations if it can’t reliably access systems, respect permissions, and execute workflows under real-world constraints. MCP provides the language. Workato is trying to provide the infrastructure.
If successful, this could mark a turning point where AI initiatives finally escape pilot purgatory—and start delivering the operational impact enterprises have been promised for years.
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