Plain-language definitions of the technology, the architecture, and how Kivo validates AI in regulated environments — updated to reflect Kivo’s Headless GxP™ platform and AI Validation Position Paper v11.
It is not a feature, a chatbot, or a private model. It is the connective tissue that lets your AI ecosystem interact with Kivo without compromising the controls that make Kivo defensible and compliant.
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Headless GxP is built around a three-party relationship: a human user, an AI agent, and the Kivo MCP service. The architecture is designed so that each party does what it is best at, and so that the regulated action - the creation, modification, or approval of a record - is always taken by an authenticated human..
The user authenticates into Kivo from her AI client using her existing credentials. All actions are governed by the permissions of the authenticated user.
The user asks the agent to perform a task or analyze data in Kivo. The agent calls Kivo's MCP service to understand the actions it has access to, as well as the roles and permissions of that user.
The agent reasons over the returned content and surfaces a response to the user. This could be a list of documents, a report, or a suggested action with a link to Kivo to complete the task.
Through Kivo's structured handoff pattern, the agent passes the proposed action back into the Kivo UI. The user reviews, edits, and executes under her own identity. Kivo captures all actions in the audit trail.
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"AI does the analysis. The human does the work. Kivo records both."
— Kivo AI StrategyKivo validates the interface, not the AI-generated outputs. Concretely, that means we validate the MCP service's permissions enforcement, its data segregation, and its operational controls. .
AI outputs are classified as unverified decision-support content. They become GxP records only after a qualified person reviews, evaluates, and approves them.
Kivo applies 21 CFR Part 11 controls to system functionality and regulated records. In a headless model, those controls are enforced at the interface layer rather than the UI:
Every agent action is governed by the permissions of the authenticated user and the tools accessible by the MCP service
Every AI-assisted action is logged in the Kivo audit trail, which is computer-generated and immutable (§11.10(e))
No AI action becomes a GxP record without qualified human approval
Validation is proportionate to risk; AI is classified as decision-support with no autonomous impact on GxP records
Kivo validates the AI interface, and maintains that validated state through a change control and OQ testing process. Customers are not required to repeat Kivo’s platform-level validation.
Customers retain full responsibility for vendor qualification, risk assessment, and change management of the AI tools they connect — exactly as they do for every other tool in their qualified stack. The FDA's CSA guidelines give organizations a roadmap to right-size that effort.
Kivo’s approach validates the controls that govern the agent’s access and ability to act in Kivo. It applies regulatory principles to AI without overextending validation expectations onto non-deterministic outputs.
This is the architecture that regulators have consistently asked for — not a guarantee that AI is always right, but a guarantee that humans are always accountable and that the system always knows who did what.
Kivo's validation position is built to satisfy current and emerging frameworks concurrently:
This page summarizes Kivo's AI Validation Position Paper (v11.0). The full position paper, including the dual-layer audit trail specification, delivery-surface validation requirements, and the complete regulatory reference set, is available to customers as part of the supplier qualification package.
We'll walk through the validation framework, demo agent-native workflows, and answer questions live.