Three teams. One metric.
Three different numbers.
of US companies don't trust the data feeding their AI agents.
2024 enterprise data governance survey
What if the number came with a receipt?
A machine-readable contract that says "this number is trustworthy, and here's the proof."
Anatomy of a trust contract
A trust contract is a machine-readable artifact that tells you (and your AI agents) whether a metric can be trusted right now. It carries the signals that vouch for a number — lineage, freshness, ownership, and more — and scores the measurable ones into a single grade. Toggle the factors below to see how trust degrades.
Try turning off "Data freshness" below and watch the grade crater.
Monthly Recurring Revenue
Total recurring subscription revenue for the current month at Meridian Analytics.
Consumption confidence
The factors above are static signals. They describe the metric itself: its lineage, owner, freshness, definition. The trust layer also tracks a feedback signal called consumption confidence: how confidently real humans and agents actually act on the score. When a definition changes, CC resets proportionally. A cosmetic tweak barely moves it; a formula rewrite zeros it out.
See how CC responds to changeFreshness, lineage, and ownership are three of the signals a trust contract carries. The next section lays out the full set — and what each combination of signals lets you do.
What makes a metric trustworthy
Six signals travel with every number. What you can do depends on which ones hold.
A governed metric carries a trust contract — six signals that say where it came from, when it was last computed, who owns it, and how confidently it's been used. Each signal you have unlocks a different move. Stack them, and the number becomes safe to act on — for a person or an agent.
* One signal alone can mislead. A widely-used number feels trustworthy, but popularity is not provenance — the platform calls that false trust, which is why usage never appears as a lobe here. Only the signals that vouch for the number itself move it toward the center.
The trust contract — six signals that travel with every metric
Five describe the number; one is feedback from how it's consumed. The MVP ships the first three — enough for a basic reliability check. The diagram maps those three plus consumption confidence.
where the number came from
when it was last computed
who to contact if it looks wrong
can it be found in the catalog
how widely it's relied on
how confidently it's consumed
Diagram structure inspired by David McCandless, “What Makes a Good Visualization?”
When trust breaks
Trust doesn't fail in isolation. One broken link degrades every metric downstream. Trigger a failure to see it propagate.
Click a failure scenario below to see trust degrade across the metric chain.
The same contract, two consumers
A human reads the trust grade and decides whether to act. An AI agent reads the same contract and decides autonomously. Drag the slider to see how trust level changes behavior.
"Grade A. I can confidently include this in the board deck."
{ "metric": "MRR", "value": "$4.2M", "trust_score": 92, "grade": "A", "action": "auto_execute" }
Agent will proceed autonomously. No human review needed.
Drag the slider below 70 to see the agent halt. Above 90, it acts on its own.
Building the trust layer
Three gates every organization needs to pass before its data can power autonomous decisions.
Where is your organization? Select a gate.
Three layers of agent governance
Most organizations have layer one. Almost nobody has layer three.
Select a layer to see an example.
"If ESPN can agree on LeBron's stats across every broadcast,
your company can agree on MRR."
The ESPN test for data trust
The trust layer isn't a product. It's a thesis about how organizations should structure the relationship between data, decisions, and autonomy.
Read the full thesis on Substack →