AI Lens

Help AI understand your company.

• Who it is for: Teams deploying autonomous AI agents, plus the security and platform owners accountable for what those agents can reach and change. This lens is primarily consumed by machines, not by a person at a dashboard.
• The question it answers: Help AI understand the company before it acts — what can agents reach and change?

How you onboard the use case

You onboard the AI Lens by onboarding the 7lens Ontology and then exposing it to agents through API and MCP in your own environment — this is a machine-consumed Lens, not a human dashboard.

1. Define what agents may reach

Decide which organization, business, application and technology Context each agent needs, and the classifications and permissions that bound it.

2. Govern access and audit

Apply the customer’s permission and audit model so every agent action is authorized and recorded, exactly as for a person.

3. Build the Lens API / MCP

The customer creates the AI Lens as an API and/or MCP server in their own environment. API and MCP let AI agents consume the Lens, and other Lenses can also be exposed through API and MCP when required — but Lens 07 is primarily designed for this machine-to-machine consumption. 7lens provides a reference architecture for building the API/MCP implementation in the customer’s own environment, with native integration with the 7lens Ontology, and agent access stays governed by the customer’s permissions and audit model.

What 7lens connects

The AI Lens gives an AI agent a governed understanding of the enterprise Context: the relevant organization, business structure, applications, technology, relationships, classifications, permissions, and the context needed to understand what an action means. Agents consume it through the API and MCP, under the customer’s permissions and audit trail.

• Organization
• Business structure
• Applications
• Technology
• Relationships
• Classifications
• Permissions
• Action context

Relationship chain: Agent → API / MCP → Context → Permissions → Audited action

An agent reads the Context through the API and MCP, within the customer’s permissions, and every action is governed by the same audit model as a person.

The problem

Autonomous agents are being given control of enterprise systems before they understand the enterprise. Without a governed reading of the Context — the organization, business structure, applications, technology, relationships, classifications and permissions — an agent cannot know what an action means or what it is allowed to touch.

Give your agents an understanding of your enterprise before you give them control of it.

The outcome

An autonomous agent understands the enterprise before it acts: what it can reach and change, what each action means, and the permissions and audit that bound it. Lenses 1–6 are built primarily for people with API/MCP also available; Lens 7 is built primarily for AI agents, with API and MCP as the principal consumption mechanism.

The value

Give your agents an understanding of your enterprise before you give them control of it. The Lens is consumed machine-to-machine through API and MCP, under the customer’s permissions and audit model — not as another dashboard.

Reference use case

The seven named Lenses are reference use cases: starting points you configure, combine, and extend. A lens is a reading of the same Context, not a dashboard and not a separate product module.

Next steps

• Onboarding in under 40 hours: configure the Context every lens reads.
• Data model: the Ontology behind each lens.