The shared layers
Stop the swivel chair. Govern from one seat.
Policy, Guardrails & Identity
Cost, FinOps, Token Management & Observability
AI Registry & Catalog
Workato AI Control Planes
Govern every model call, every tool an agent touches, every agent action, and every API request. All from one platform, whether running on Workato or passing through.
Key Components
Three components to control your entire AI Footprint
AI Gateway
One governed layer in front of every model, every MCP tool, every agent, and every API request. Centralized controls for your Model, MCP, Agent and API traffic, all behind one identity model, one policy model, and one audit trail, so a rule written once applies everywhere.
AI Registry & Catalog
One place to publish, discover, and reuse every model, tool, MCP server, and agent in the company, with clear ownership and controlled access. Teams stop rebuilding what already exists, and IT stops guessing what's running.
AI Security & Governance
One policy model across every gateway layer. PII masking, prompt-injection screening, content moderation, and grounding checks stay consistent. Enforce one identity model and view consolidated cost and consumption. Set a rule once; it resolves most-restrictive-wins everywhere it applies.
How it works
You can't govern the AI you don't know exists
The Workato Control Plane gives you one registry of every model, agent, and tool in your company, one set of policies and identities that applies to all of it, and one view of what it's costing you - whether it's built on Workato or just passing through. And because Workato also sits on the execution path, that control doesn't stop at the prompt. It follows the request into the record it writes, the system it queries, the data it changes.
What each team can do with it
IT Governance
Set one policy for models, tools, and agents, and revoke access in one place instead of five.
Security & Compliance
Mask PII, screen for prompt injection, and produce one audit trail covering every AI action.
Finance Operations
See AI spend by team, key, agent, model, and tag, set budgets, and charge it back.
Platform/AI Engineering
Give every team one governed path to models, tools, and agents instead of eleven private setups.
Application/Product Engineering
Give agents governed access to real business systems instead of hand-building integrations per agent.
Data & Analytics
Let agents query warehouses and lakes under the same access policy as every other consumer.
Business Operations
Find and use approved agents and MCP servers from a catalog instead of filing a ticket.
Frequently asked questions
What is an AI Control Plane?
An AI control plane is the layer where an organization governs everything it does with AI from one place: which models can be called, which tools and systems an agent can reach, which agents exist and who owns them, what policies apply, who is allowed to do what, what it all costs, and what happened. The distinction that matters is control plane versus data plane. The data plane is where the work runs - the model inference, the tool execution, the agent loop. The control plane is where the governance lives. Separating them is what lets you change a policy without redeploying an application, and what lets one rule cover traffic from a dozen teams using a dozen tools.What’s the difference between an AI Control Plane and an AI Gateway?
A gateway is an enforcement point. A control plane is where you decide what gets enforced, across every enforcement point you have. At Workato, the AI Gateway is that enforcement point - one governed layer comprising four gateways: Model, MCP, Agent, and API. The Control Plane is everything above it: the registry and catalog of what's approved and reusable, and the security and governance layer - policy, identity, cost, and audit - that spans the AI Gateway and any AI built or run outside Workato.How is this different from a point AI/LLM gateway?
A point gateway governs the model call and stops there - it has no visibility into what an agent does with the model's answer, or which system a tool call reaches next. Because Workato sits on the execution path - the same platform that runs the write, the query, the update against your actual business systems - its Control Plane governs the action, not just the prompt.Does an AI Control Plane govern AI that I didn't build?
That is most of the point. Agents and AI clients built elsewhere, like a coding assistant, a chat client, or an agent framework, route their model and tool calls through the Control Plane and inherit the same access controls, guardrails, limits, and audit trail as anything built in Workato. Third-party MCP servers can be brought under governance by proxying them. Nothing has to be rebuilt in Workato to be governed by it.Is an AI Control Plane the same as an AI platform or an MLOps platform?
No. An AI platform is where teams build and run AI. A control plane governs what those platforms and tools are allowed to do. They're complementary - most enterprises will have several of the former and should have one of the latter.Can I start with one gateway and add the others later?
Yes, and most customers do. Because the registry, policy model, identity model, and cost view are shared across all four gateways, adding a second or third does not mean a second governance setup.What audit trail does the AI Control Plane produce for compliance?
Every interaction is recorded: who invoked it, which model, tool, or agent, what policy applied, whether it was allowed or blocked, and what it cost. Records link Control Plane activity to downstream execution detail, so an action traces from the prompt through to the record it changed in a business system, and can stream to your SIEM, which is what EU AI Act readiness, SOX, and HIPAA programs ask for.