Why Application Agents Hit a Wall and How Workato MCP Breaks Through
AI agents are showing up everywhere. Salesforce has Einstein Copilot. Zendesk has its AI agent. ServiceNow has Now Assist. These tools are becoming standard in modern workflows, and it’s easy to see why. They promise higher productivity, tighter integration inside their own applications, and faster completion of everyday tasks.
But teams quickly discover the limits. The moment an agent needs to access data or trigger actions in another system, it stops short. A Zendesk agent can’t pull billing details from NetSuite. A Salesforce agent can’t verify a support entitlement without reaching into ServiceNow. The challenge isn’t building the first agent—it’s connecting them across the enterprise in a way that’s secure, scalable, and governed.
This is the wall most organizations hit. And it’s exactly where Workato Enterprise MCP provides a breakthrough.
The Rise of Vendor-Specific Agents
Application-specific agents are embedded directly inside business applications. Their value is immediate. They can summarize CRM notes, prepare ticket responses, generate insights, and trigger lightweight actions without leaving the tool where employees already work. They’re fast to set up and fully supported by their respective vendors.
The proliferation is staggering—and expensive. Every major enterprise platform now ships with its own AI agent:
| Platform | AI Agent/Copilot | Primary Function |
|---|---|---|
| Adobe | Firefly | Creative generation, PDF assistance, content editing |
| Asana | Asana Intelligence | Project planning, workflow optimization, status updates |
| Atlassian | Rovo | Knowledge search, project assistance, documentation |
| Box | Box AI | Content intelligence, document Q&A, metadata extraction |
| ClickUp | ClickUp Brain | Task management, project updates, document generation |
| Dropbox | Dash AI | File search, content summarization, organization |
| Gemini | Email drafting, document generation, data analysis | |
| HubSpot | ChatSpot | Marketing automation, content generation, CRM assistance |
| Microsoft 365 | Copilot | Document creation, data analysis, meeting summaries |
| Monday | Monday AI | Workflow automation, project insights, task suggestions |
| Notion | Notion AI | Note-taking, writing assistance, information synthesis |
| Oracle | Oracle AI | Database optimization, ERP automation, supply chain insights |
| Salesforce | Einstein Copilot | CRM assistance, sales forecasting, customer insights |
| SAP | Joule | ERP guidance, procurement support, financial analysis |
| ServiceNow | Now Assist | IT service management, workflow automation |
| Slack | Slack AI | Channel summaries, search, conversation recaps |
| Workday | Workday AI | HR processes, talent insights, financial planning |
| Zendesk | AI Agent | Customer support automation, ticket resolution |
| Zoom | AI Companion | Meeting summaries, action items, chat assistance |
The pattern is clear: every major enterprise platform is deploying its own AI agent. But each operates in isolation, creating silos instead of orchestration.
For enterprises running hundreds of applications, this creates both a capability explosion and a coordination crisis. Each agent operates in isolation. None can orchestrate work across systems without custom integration.
The Hidden Cost of Siloed Agents
At first, isolated agents feel like progress. They give teams quick wins and spark enthusiasm. But as more agents appear in the organization, the cracks begin to show.
Workflows begin to fail. Broken automations appear when one agent updates data in one system and another overwrites it because neither has full visibility of the workflow. Teams end up with inconsistent records, partial updates, and processes that fail silently.
Data silos deepen. Each agent only sees its own system, which means critical information stays locked inside a single application. Agents make decisions based on incomplete or outdated context.
Poor context leads to poor outcomes. Without a unified view, agents often improvise their way through tasks. They repeat steps, misread situations, or trigger actions that should have involved checks or approvals.
Over time, organizations accumulate duplicated logic, rising maintenance costs, and a sprawling mix of disconnected AI tools. The problem is not the model. The problem is the missing orchestration layer underneath.
Why APIs and Raw MCP Aren’t Enough
Many teams try to solve cross-system agent coordination with APIs or the emerging Model Context Protocol (MCP). Neither bridges the gap.
APIs expose endpoints, not business processes. A password reset requires eight API calls across three systems with conditional logic, retries, and audit trails. Asking an AI agent to orchestrate these steps by calling raw APIs introduces unpredictability, risk, and compliance gaps. APIs also lack enterprise safeguards—no identity inheritance, role-based access, transactional integrity, or persistent context.
Raw MCP provides protocol access, not enterprise readiness. MCP standardizes how agents discover and call capabilities, which solves the connectivity problem at the protocol layer. But raw MCP servers typically expose the same granular API operations with the same gaps: no governance, no audit trails, no identity enforcement, and no business context. An MCP server that wraps a REST API is still just an API—now with a standard interface.
The problem isn’t connectivity. It’s orchestration, governance, and trust.
A business process like “reset password” or “process refund” isn’t a single API call or MCP tool. It’s a stateful, multi-step workflow that requires validation, error handling, rollback capabilities, and audit logging. It needs to run with the right permissions, in the right sequence, with the right safeguards.
APIs move data. Raw MCP standardizes access. Workato Enterprise MCP orchestrates intelligence with the governance, security, and business context that production systems require.
Workato Enterprise MCP: The Missing Link Between Agents and the Enterprise
Workato Enterprise MCP fills the gap between AI reasoning and real business action. It is the orchestration layer that turns raw API calls into secure, governed, and accurate business operations that agents can rely on.
It brings together three essential capabilities:
| Trust & Security | Every agent action is authenticated, authorized, and auditable. MCP provides runtime user authentication where agents inherit real user permissions. It includes role-based access control, automatic PII masking, field-level security, and immutable audit logs. Compliance standards such as SOC2, PCI, and ISO are built directly into the platform. With Enterprise Trust, AI becomes safe for production. |
|---|---|
| Orchestrated Context | Workato offers a governed, stateful runtime that coordinates data, logic, and reasoning across all systems. It maintains persistent context, handles multi-system coordination, and ensures transactional integrity so processes complete cleanly or roll back predictably. This gives AI a continuous view of the workflow, which is something raw APIs cannot provide. |
| Enterprise Skills | Skills are proven, reusable units of real work such as process refund, onboard employee, or create quote. Each Skill includes logic, approvals, error handling, retries, and guardrails. They give agents dependable building blocks that produce the same result every time. Skills eliminate guesswork and turn AI decisions into accurate enterprise actions. |
Together, these capabilities transform MCP from a simple access layer into the foundation for secure, scalable, and accurate agentic systems.
Conclusion: The Future Is Interoperable
Application-specific agents will always reach their limits inside closed ecosystems. To unlock their full potential, organizations need a foundation where agents, humans, and systems operate together with shared context, trust, and governance.
Workato Enterprise MCP provides that foundation. It turns isolated agents into a connected network of intelligent, enterprise-ready operators that coordinate across your entire business.
This is how organizations move beyond early AI pilots and into production-grade, agentic operations that deliver measurable outcomes. And it is how they become part of the five percent of companies that succeed in bringing AI to life at enterprise scale.
