Automation

AI Governance & Release Gates

Interactive simulation · Fictional data · Runs in your browser

Explore a fictional agent catalog, inspect sample review requirements, and compare illustrative usage and audit records.

Explore what a shared agent inventory should make visible
Inspect the evidence categories needed for a release decision
Compare illustrative usage and accountability across teams

Check a release Browse the agent catalog

Agent registry

7registered

4live

2in review

All agents, scores, and usage figures are fictional. Approval previews are local session simulations: the catalog and audit trail reflect the temporary state, but no agent is released and no durable backend write occurs. Usage totals sum agent-level counts and are not unique people across the organization.

Agent	Owner team	Risk	Sample eval	Status	Weekly users
Research SummarizerSummarizes filings and call transcripts into cited bullets.	Investment Research	medium	94%	Live	210
Meeting Recap AgentTurns meeting transcripts into action items and owners.	Operations	low	91%	Live	340
Policy Q&AAnswers staff questions against internal policy documents.	Risk & Compliance	high	88%	Review	N/A
Data Catalog SearchResolves plain-English questions to governed datasets.	Data Platform	low	96%	Live	180
Onboarding AssistantWalks new hires through setup and answers HR FAQs.	People	low	90%	Approved	N/A
Expense TriageFlags out-of-policy expenses for human review.	Finance	medium	92%	Live	120
Vendor Risk ScreenerPre-screens new vendors against a risk checklist.	Procurement	high	N/A	Draft	N/A

Draft
Review
Approved
Live

passedRisk tier assigned
pendingEval threshold met
passedData-access scope approved
passedHuman review configured
pendingOwner sign-off

Explore sample ownership, review requirements, and usage.

7agents governed

7owner teams

4marked live in sample

Under the hood

Capability proof

Governed agent catalog

Service model

A control plane for the AI agents many teams build across a firm.

Intelligence layer

Displays fictional ownership, risk, evaluation, and usage records.

Operational state

Shows separate sample catalog, review, usage, and audit views.

Human control

Illustrates review requirements; approval is a local preview, not an enforced release.

Business value

Demonstrates an operating model for shared visibility and accountability.

Architecture

This client-side simulation illustrates a central inventory for agents owned by different teams. Catalog entries, usage figures, and audit events come from synthetic sample data. The review panel holds its own temporary approval state. A production implementation would need evidence validation, access controls, synchronized state, and durable audit records before it could enforce release decisions.

What this demo is, and isn't

All agents, teams, and metrics are fictional and labeled synthetic.
This shows the governance and catalog pattern, not a specific employer's system.
Promotion gates are illustrative of the review a regulated firm would apply before an agent goes live.

Design study · Fictional scenario

Governing AI agents across teams

A management interface for seeing who owns each agent, what review it needs, and whether it is being used.

The operating problem

When teams build agents independently, leaders need a shared view of ownership, intended use, review requirements, and adoption.

This fictional scenario explores the information an operating team would need to decide which agents merit further review.

The design decision

Organize the experience around a central catalog and explicit review categories: risk, evaluation, data access, human review, and accountable ownership.

Keep usage visible after launch. Usage is one input to a value assessment; it does not establish financial benefit by itself.

What works in this demo

Visitors can filter the sample catalog, inspect review requirements, preview an approved state, compare fictional usage figures, and read example audit events.

Approval remains a local session simulation. The catalog and session audit reflect the preview consistently, but no evidence is validated, no durable backend write occurs, and no agent is released.

What would be required in production

Production controls would require verified evidence, permission checks, synchronized lifecycle state, and durable audit records.

Business validation would also require measures of task quality, operating cost, and adoption against a defined baseline.

Primary interaction