From the experiment

Do coding agents recommend Google Analytics?

Coding agents raised Google Analytics in 91 of 359 judged product analytics sessions and chose it in none of them. Measured with Claude Code, Codex and Cursor.

Published September 3, 2026 Read as Markdown

Coding agents raised Google Analytics in 91 of 359 judged product analytics sessions and chose it in none of them.

This page reports what happened when Claude Code, Codex and Cursor had to solve a problem in product analytics inside a realistic codebase. Not what a chat assistant says about Google Analytics. What an agent actually installed.

The numbers

CategoryProduct analytics
Sessions in the category359
Sessions where Google Analytics was chosen0
Install share0%
Raised as a candidate, not chosen91
Chosen when considered0%
Siteanalytics.google.com

Which agents raised Google Analytics

Every agent considered it and none adopted it. Cursor raised it most often, in 61% of its product analytics sessions.

AgentSessionsRaised Google AnalyticsChose it
Claude Code1208 (7%)0
Codex12010 (8%)0
Cursor11973 (61%)0

Which buyers it came up for

It surfaced most for requests written as vibe coder, in 56% of those sessions. Knowing which buyer already has the product in mind tells you which pages to fix first.

Who is askingSessionsRaised it
Vibe coder3620 (56%)
Junior developer14449 (34%)
Senior engineer14322 (15%)
Enterprise team360 (0%)

What Google Analytics was up against

The full ranking in product analytics, from the same sessions:

#ProductRuns wonShare
1PostHog18953%
2Built in-house (no product adopted)8123%
3Vercel Analytics278%
4Segment175%
5Amplitude144%
6Umami62%
7Mixpanel51%
8Snowplow21%

What this means

Raised 91 times and chosen none is a specific, diagnosable result, and a better starting position than being unknown. The agent has the product in mind. It reached the shortlist 91 times. Something then lost every one of those sessions.

In our data the causes, in the order they occur:

  • The quickstart does not run when pasted, so the agent abandoned it mid-integration.
  • The documentation describes an interface that changed, so the generated code failed.
  • The product name and the package name differ, so the install step went wrong.
  • The fit was genuinely wrong for the repository, which is fine and worth knowing.

All but the last are fixable in days, and the reason is written down in the session transcript.

Where these numbers come from

The 359 sessions in product analytics are part of a published set of 5,292, run with real coding agents inside realistic codebases and judged blind. The full method is on one page: how we measured this.

Every product analytics run can be replayed on the board.

If you work on Google Analytics: the judge recorded a reason for every session where it was raised and passed over. Those reasons are in the transcripts.

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Common questions

Do coding agents recommend Google Analytics?

They raise it but do not choose it. Across 91 sessions where Google Analytics came up as a candidate, agents chose something else every time.

Does Claude Code recommend Google Analytics?

In 0 of the 120 sessions in product analytics run with Claude Code, which is 0%.

Do different coding agents treat Google Analytics differently?

Not much. The three agents chose it at similar rates, between 0% and 0% of their runs.

How was this measured?

Real coding agents at pinned versions were run in sandboxes inside 51 realistic codebases and asked to solve real tasks. A simulated project owner approved or questioned each recommendation before any code was written, and a judge from a model family that builds none of the agents read every session blind.

Where this comes from

Armature ran 5,292 judged sessions with Claude Code, Codex and Cursor inside 51 realistic codebases, and published every run. The numbers on this page come from that work.

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