As enterprises look to bring AI agents and LLMs into their operations, one question looms large: which integration platform can truly deliver at scale?
Both Workato and Boomi are racing to empower businesses for this new era of intelligent automation through the Model Context Protocol (MCP) — a framework that allows AI agents to discover and safely invoke automation logic.
But while Boomi’s MCP story remains mostly on the roadmap, Workato’s Enterprise MCP is already here — production-ready, proven, and powering real enterprise AI use cases today.
In this deep dive, we’ll explore how the two platforms compare, and which key questions every enterprise should be asking when choosing their AI integration foundation.
The MCP-Ready Foundation: From Announcements to Reality
The Model Context Protocol (MCP) is rapidly becoming the new standard for agent-to-automation communication — enabling AI to safely discover and invoke enterprise logic.
Workato’s Enterprise MCP is already fully aligned with that vision. Every Recipe, API, or Genie in Workato can be instantly exposed as an MCP “skill,” allowing AI agents to connect with enterprise automations securely and consistently — no custom code required.
By contrast, Boomi’s MCP Server connector has been announced but is not yet generally available. For Boomi users, enabling agent access still involves building custom wrappers or SDKs — adding complexity and risk.
A revealing question to ask here:
Can your platform expose existing automations as agent-callable MCP servers without custom code?
If the answer is no, that means your teams will spend more time maintaining glue code than innovating.
Another key consideration:
How does your platform standardize LLM or agent access to integration logic?
Workato has embedded that standardization directly into the platform. MCP endpoints are secure, observable, and fully governed. With Boomi, this capability remains an aspiration — not an operational feature.
Governance and Security: Control, Confidence, and Context
When automation shifts from humans to AI agents, governance and security become mission-critical. Workato brings enterprise-grade discipline to this layer, offering the fine-grained control that complex organizations require.
Workato’s role-based access control (RBAC) extends to every recipe, API, and environment. Policies can be inherited across workspaces, enforcing least-privilege access without manual oversight. Boomi, while offering roles and entitlements, still depends on human configuration and documentation to maintain consistency.
Ask yourself:
How do you enforce least-privilege access when hundreds of automations run across business units?
Workato’s governance engine ensures those policies are programmatic and repeatable — not reliant on tribal knowledge.
The same rigor applies to observability. Workato provides a centralized audit trail for every MCP invocation, user action, and system event. Logs are detailed, contextual, and SIEM-ready. Boomi’s audit visibility, meanwhile, is limited to individual environments with only 30 days of data retention — insufficient for most compliance teams.
Enterprises serious about compliance should ask:
Can your platform trace each agent action to the user, role, and system context that triggered it?
and
How do you provide audit-ready logs for every AI-initiated automation?
Security, too, is an area where Workato leads. With encryption, data masking, Bring Your Own Key (BYOK), and automatic token rotation, Workato offers secure-by-default architecture. Boomi supports encryption and external vaults, but its configuration is often manual — a risk at enterprise scale.
Identity propagation is another make-or-break factor. Workato integrates deeply with Okta, Azure AD, and other IAM systems, maintaining user identity context throughout every automation. Boomi supports SSO but lacks end-to-end “on behalf of” identity propagation.
In evaluating readiness for AI-driven workloads, ask:
Does your platform preserve user identity context when an LLM or agent triggers a workflow?
and
Can IAM enforce conditional access or step-up authentication for high-risk automations?
Governance isn’t just about control — it’s about trust. And in AI automation, trust is everything.
Scaling Intelligently: Observability, Cost Control, and Deployment Safety
As AI agents begin invoking thousands of automations per hour, scalability and visibility become inseparable. Workato’s serverless architecture delivers both.
Workato’s Enterprise MCP scales elastically, with transparent consumption tracking at the recipe, skill, and department level. That means every agent action can be measured, attributed, and budgeted — turning AI automation into an auditable, forecastable function.
Boomi, on the other hand, relies on infrastructure-based scaling and provides limited cost monitoring. Without granular visibility, enterprises risk losing track of which workloads or agents are driving usage and cost.
It’s worth asking:
How can I forecast the cost per ‘agent action’ or per department?
and
What visibility do I have into which AI agents are driving consumption?
For most enterprises, Workato’s answer — precise, transparent, and automated — is the difference between scalable AI adoption and uncontrolled sprawl.
Deployment safety is equally crucial. Workato enforces true environment isolation, using separate keys, tokens, and audit scopes for dev, test, and production. This prevents privilege leaks and supports secure promotion between environments.
Boomi’s isolation is logical, depending on runtime configurations that can blur boundaries between environments.
To stay compliant and resilient, ask:
How are development, staging, and production MCP servers isolated from each other?
and
Can policies or connectors be promoted securely across environments without redeployment risk?
At scale, these are not minor details — they’re the difference between innovation and incident.
Reusability and Speed to Value: Turning Automations into AI Skills
AI adoption doesn’t just depend on what you can build — it depends on how quickly you can reuse what you already have.
Workato’s Enterprise MCP makes that effortless. Any Recipe or API can be instantly published as an MCP skill, ready to be discovered and reused by other teams or AI agents. This means business users can safely contribute to enterprise automation without waiting on developer bandwidth.
Boomi requires custom API development or Boomi Flow, introducing delays and technical overhead.
When evaluating time-to-value, ask:
How quickly can we expose an existing integration to an AI agent without developer intervention?
and
Can business users publish automations as reusable AI skills securely?
This ability to democratize AI automation — securely — is where Workato truly differentiates.
On the governance side, Workato’s centralized policy engine defines who can create, deploy, or invoke automations, and enforces those rules automatically. Boomi relies on distributed governance across its API Control Plane and Agent Control Tower, which requires manual alignment and documentation.
For large organizations, that’s a scalability bottleneck.
Enterprises should also ask:
How are automation policies enforced without developer scripts?
and
Can governance controls be audited centrally across all automations and agents?
With Workato, those answers are yes — because automation governance isn’t optional; it’s built in.
The Verdict: Enterprise MCP Is Here — and It’s Workato
Boomi is preparing for the MCP era. Workato is already leading it.
From MCP readiness and governance to scalability, security, and speed to value, Workato’s Enterprise MCP gives enterprises the framework they need to operationalize AI automation today — not someday.
When evaluating platforms, look beyond roadmaps and marketing. Ask the questions that expose true readiness:
- Can it expose automations as MCP skills without code?
- Can it trace every agent action back to its origin?
- Can it scale transparently across teams and budgets?
- Can business users safely publish automations as AI skills?
If the answer isn’t yes across the board, it isn’t enterprise-ready.