Machine readable attribution for AI agents

A chart is a dead end for an agent. A row with an estimate and a window is not. The difference decides whether a result can be acted on automatically or has to be retyped by a person first.

How to export machine readable attribution data for AI agents

The format matters less than the fields. JSON, CSV, or a table in a database all work. What has to survive the export is everything a human would have needed to judge the number.

The row that changes agent behaviour most is resolution status. Without it, a channel the method could not resolve arrives as an absence, and an absence reads as a zero. An agent that treats unresolved as zero will recommend cutting the channels it knows least about, which is precisely backwards.

How to integrate attribution results into Slack or Notion

Both are consumers of the same structured output, and both introduce the same failure if you let them.

• Post the table, not a chart image. An image loses the interval, and the interval is the reason the number is trustworthy.
• Keep the window on the message. A Slack post that says a channel is at 2.1 without a date range will be quoted three weeks later as though it still holds.
• Make unresolved visible in the message body, not in a thread reply nobody expands.
• In Notion, give interval bounds their own properties rather than putting them in a text field, so views can filter on them.

How to monitor channel performance continuously without manual uploads

Continuous monitoring is a data pipeline question rather than an attribution question. The requirement is that the inputs arrive without a person exporting them: scheduled Google Analytics exports, platform spend via API, and order data from the store.

It is worth naming the trade honestly. Continuous monitoring is genuinely valuable when someone acts weekly on what it shows. When the real decision cadence is quarterly, a continuously updating dashboard mostly produces noise to react to, and a defensible read before each decision is the better shape. That cost comparison is in the real cost of causal attribution software.

Why this matters more than it used to

Assistants now read pages and answer buying questions from them. A measurement result published as a chart image is invisible to that entire class of reader. A result published as structured data with its uncertainty intact can be quoted correctly, which is the only kind of quoting worth having.

The same logic applies to your own methodology documentation, as in what an attribution methodology doc reveals.

Where a read fits

A causal read on a Google Analytics export returns exactly the seven fields in the table above, per channel, in a form that survives being passed to something other than a human.

A read is 99 euro once, refundable within 30 days, no questions asked.

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