What can a machine buyer do with the evidence a score throws away?
From MCC-001, an essay: a hypothesis, not a measurement.
The short answer
Use it. The essay argues that a machine could examine structured evidence of performance under comparable conditions, tell an old observation from a current one and attach different costs to the same failure — the details a rating throws away to save human attention.
As published in the issue
A rating economizes on human attention by compressing a record to a single or simple set of numbers. It can also create comparability, embed specialist judgment and give participants a common vocabulary. For a machine, the discarded details may be the useful parts. It could examine structured evidence of performance under comparable conditions, distinguish an old observation from a current one and attach different costs to the same failure.
The sentence above is quoted from the essay as published and is not re-written here. The essay, its date and the address it was published at are below.
Why it matters
A rating is shaped for a reader with limited attention. A machine reader has a different constraint, and the compression that helps a human may discard what the machine could have used.
What we measured
Nothing here was counted. What our measured series publishes about how fresh published labels are, and about routing an agent on one, is linked below; the essay's own tests have not run.
What the evidence shows
- Issue
- Machine Commerce Conditions: MCC-001 — September 2026
- Published
Where our own record speaks to this
These are measured answers, each counted over its own population on its own date. This page links them and quotes nothing from them.
How fresh are x402 health labels? · What happens when an agent routes on a health label?
Method and reproduction
The terms
The terms this question uses are defined in the glossary.
Corrections and disputes
Corrections to this issue are on the corrections register, and this answer reads from the record rather than from a copy.