RiskMandatecustomers ↗ riskmandate.ai first batch, being set up v0.1.1
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The usage moment, and the three questions behind it.

The point of the programme is to find the moment an Agent Behaviour Policy becomes worth something to a particular person. Three questions follow from it. This page holds them, and the findings as they come in, counted from the follow-ups rather than remembered.

Status, from docs/reality.md: shipped v0.1.0 The three questions the programme exists to answer, with a findings table that starts empty

The three questions

Q1What is the simplest useful behaviour policy?

One agent, a few rows, the gap found. Perhaps a system prompt turned into rows, as in system prompts as behaviour policies; perhaps a skill checked against its mandate; perhaps one agent's reach, measured.

Q2What is the entry point with least friction?

A reader once told us the pages were hard to understand. The start-here page has to fix that, and each customer will show us where it does not.

Q3What is the price?

The credit tells us how much each customer uses before they would top up, and the follow-up tells us why.

What we track, per customer

The things are simple, and they are on every customer page: when they started, when they wrote their first behaviour policy, how many rows and gaps it has, which milestone steps are done, what they used of the credit, and whether they topped up. The batch table on the customers page is those fields, one row per customer, and the counts on the home page are their sums.

Findings

A finding is a pattern across customers, read from the second follow-up answer, with the number of customers who hit it. The table starts empty on purpose; a finding typed before it was counted would be a guess.

QuestionFindingCustomersFirst seenWhat we did
None yet. The first batch started on 2026-10-20.

What a finding changes