Do coding agents recommend Traversal?
Coding agents raised Traversal in 61 of 179 judged ai sre sessions and chose it alone in none of them, and only as part of a joint choice in 1.
Coding agents raised Traversal in 61 of 179 judged ai sre sessions and chose it alone in none of them, and only as part of a joint choice in 1.
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 Traversal. What an agent actually installed.
The numbers
| Category | Ai sre |
| Sessions in the category | 179 |
| Sessions where Traversal was chosen | 0 |
| Install share | 0% |
| Raised as a candidate, not chosen | 61 |
| Chosen when considered | 0% |
| Site | traversal.com |
Which agents raised Traversal
Every agent considered it and none chose it alone. Claude Code raised it most often, in 62% of its ai sre sessions.
| Agent | Sessions | Raised Traversal | Chose it alone |
|---|---|---|---|
| Claude Code | 71 | 44 (62%) | 0 |
| Codex | 72 | 16 (22%) | 0 |
| Cursor | 36 | 1 (3%) | 0 |
Which buyers it came up for
It surfaced most for requests written as senior engineer, in 60% of those sessions. Knowing which buyer already has the product in mind tells you which pages to fix first.
| Who is asking | Sessions | Raised it |
|---|---|---|
| Vibe coder | 30 | 0 (0%) |
| Junior developer | 60 | 14 (23%) |
| Senior engineer | 30 | 18 (60%) |
| Enterprise team | 59 | 29 (49%) |
What Traversal was up against
The full ranking in ai sre, from the same sessions:
| # | Product | Runs won | Share |
|---|---|---|---|
| 1 | Sentry Seer | 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
Raised 61 times and never chosen alone is a specific, diagnosable result, and a better starting position than being unknown. The agent has the product in mind. It reached the shortlist 61 times and made it into 1 joint choice, but never into a choice of its own.
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 179 sessions in ai sre are part of a published set of 15,051, 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 Traversal: the judge recorded a reason for every session where it was raised and passed over. Those reasons are in the transcripts.
<!-- generated by scripts/write-data-pages.mjs -->
Common questions
Do coding agents recommend Traversal?
They raise it but rarely choose it. Across 61 sessions where Traversal came up as a candidate, agents never chose it alone; it was part of a joint choice in 1.
Does Claude Code recommend Traversal?
In 0 of the 71 sessions in ai sre run with Claude Code, which is 0%.
Do different coding agents treat Traversal differently?
Not much. Claude Code, Codex and Cursor 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 92 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 15,051 judged sessions with Claude Code, Codex, Cursor, Grok Build CLI and Muse Code inside 92 realistic codebases, and published every run. The numbers on this page come from that work.