Do coding agents recommend Sentry Seer?
Sentry Seer is the most chosen ai sre, taking 26% of 179 judged ai sre sessions. Measured with Claude Code, Codex and Cursor.
Sentry Seer is the most chosen ai sre, taking 26% of 179 judged ai sre sessions. It was also raised as a candidate in 36 further sessions without being chosen.
This page reports what happened when Claude Code, Codex and Cursor had to solve a problem in ai sre inside a realistic codebase. Not what a chat assistant says about Sentry Seer. What an agent actually installed.
The numbers
| Category | Ai sre |
| Sessions in the category | 179 |
| Sessions where Sentry Seer was chosen | 47 |
| Install share | 26% |
| Rank in category | 1 of 26 |
| Codebases it won in | 3 |
| Raised as a candidate, not chosen | 36 |
| Chosen when considered | 57% |
| Site | sentry.io |
By agent
The three agents treat Sentry Seer very differently. Codex chose it in 32% of its sessions in this category and Cursor in 8%, a spread of 24 points.
| Agent | Sessions | Chose Sentry Seer | Share |
|---|---|---|---|
| Claude Code | 71 | 21 | 30% |
| Codex | 72 | 23 | 32% |
| Cursor | 36 | 3 | 8% |
A single blended install share would report the midpoint and hide both ends. If most of your users run Cursor, your real position here is 8%, not 26%.
By who was asking
Sentry Seer does much better with one kind of buyer than another. It won 83% of sessions asked as vibe coder and 0% of those asked as enterprise team.
| Who is asking | Sessions | Chose Sentry Seer | Share |
|---|---|---|---|
| Vibe coder | 30 | 25 | 83% |
| Junior developer | 60 | 22 | 37% |
| Senior engineer | 30 | 0 | 0% |
| Enterprise team | 59 | 0 | 0% |
What Sentry Seer was up against
The full ranking in ai sre, from the same sessions:
| # | Product | Runs won | Share |
|---|---|---|---|
| 1 | Sentry Seer (this page) | 47 | 26% |
| 2 | Resolve AI | 35 | 20% |
| 3 | Datadog Bits AI Dev Agent + Datadog Bits Investigation | 26 | 15% |
| 4 | Cursor Automations | 15 | 8% |
| 5 | Claude Code GitHub Action | 11 | 6% |
| 6 | Cursor Cloud Agents | 9 | 5% |
| 7 | incident.io AI SRE | 4 | 2% |
| 8 | Cursor Automations + Cursor Cloud Agents | 3 | 2% |
What this means
Leading a category is a position to defend rather than a result to celebrate. In our data, leaders lose ground in exactly two situations: when the person asking is an enterprise buyer with procurement constraints, and when a competitor's documentation is easier for an agent to integrate correctly.
The defence is unglamorous. Keep the quickstart running. Keep the version current on the page. Keep the names aligned. Stay in the templates.
Where these numbers come from
The 179 sessions in ai sre are part of a published set of 7,852, run with real coding agents inside realistic codebases and judged blind. The full method is on one page: how we measured this.
Every ai sre run can be replayed on the board.
If you work on Sentry Seer: 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 Sentry Seer?
Yes. Sentry Seer was chosen in 47 of the 179 judged sessions in ai sre, a 26% install share, ranking first in its category.
Does Claude Code recommend Sentry Seer?
In 21 of the 71 sessions in ai sre run with Claude Code, which is 30%.
Do different coding agents treat Sentry Seer differently?
Yes, and by a wide margin. Codex chose it in 32% of its runs and Cursor in 8%.
How was this measured?
Real coding agents at pinned versions were run in sandboxes inside 90 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.
How often is Sentry Seer considered but not chosen?
It was raised as a candidate in 36 sessions without being chosen, and chosen in 47. That is a 57% conversion from considered to chosen.
Where this comes from
Armature ran 7,852 judged sessions with Claude Code, Codex and Cursor inside 90 realistic codebases, and published every run. The numbers on this page come from that work.