AI in Development
Is the team shipping at AI speed, with the quality to match?
Without AI, teams fall behind. With AI and no checks, they build up risk. CodeDD shows where each team stands, straight from Git.
98% of AI-authored commits matched a pull request — the funnel is well covered.
253,686
Lines written by AI
at least — provable from Git
9,365
Human-reviewed
4% of merged
243,958
Merged with no human review
96% of merged
16,821
Reached production
7% of merged
AI-authored
Starting volume
Merged via pull request
of AI-authored
Reviewed by a human
of merged
Approved in review
of merged
Reached production
of merged
Trusted by
What we measure
What AI wrote, who checked it, what shipped.
How much AI wrote
Lines and commits that name a coding agent. We count only what Git can prove, so the number is a conservative base.
Whether a person reviewed it
Each AI change traced from commit to pull request, review, approval and production. The key number: AI code merged with no review on record.
What is still waiting
AI-written pull requests not yet merged: how many, how old, and how many nobody has reviewed yet.
Work in flight
AI work that has not shipped.
These AI pull requests have used engineering time but have not reached production. See how many are waiting, how long they have been open, and how many have no reviewer.
AI delivery inventory
4
Open AI pull requests
68 days
Median age
3
No review yet
0
Merged, not yet in production
- <7d
- 7–30d
- 30–90d
- >90d
By repository
AI use in each repository.
See how much code AI writes in each repository, which AI tools the team uses, and whether they are set up with instructions.
- RepositoryShare written by AIUnmerged AI PRs (oldest)
cube-command-center
Claude CodeAGENTS.mdSKILL.md72%
2 (24 days)
cube-pipelines
Claude CodeNo agent instructions3%
2 (112 days)
legacy-billing
No AI tools found0%
0
Across the investment cycle
What it tells you at each stage.
Pre-deal tech DD
Check how much of the code AI wrote and how much was reviewed before merging. Both shape your view of code quality, and a high AI share belongs in the IP review.
Hold period
Track AI adoption and review coverage each quarter. Compare portcos and teams, so the ones further along can share what works with the rest.
Pre-sale preparation
Give buyers a record showing that AI-written code was reviewed like the rest of the codebase.
Connect your Git host
GitHub
GitLab
Azure DevOps
Bitbucket
FAQ
Questions.
What does “AI-authored” actually mean?
It is the share of committed work that names a coding agent. Only commits with evidence an agent wrote them enter the count, so the number is a floor — the team may be using AI more than Git can prove. Vendor dashboards that count accepted suggestions measure a different thing, and we do not add those figures together.
Why might this look lower than what the team reports?
Many tools insert code under a human’s name. If the repository does not disclose agent authorship, the share is unknown — shown as a dash, not as 0%. A repo with Cursor or Claude Code configured and no authorship trail is a visibility gap, not proof the team writes everything by hand.
What is the delivery funnel?
It follows AI-authored code from commit through merge, human review, approval, and production. Each stage answers a different question: did it go through a pull request, did a person look at it, was it signed off, and did it ship. Approval and production are compared with merged code, not with the row drawn above them.
What does “merged unreviewed” mean?
AI code that reached the default branch with no human review on record. That is the governance figure — agent-written code that skipped a person. It is only shown when the Git host actually exposes review data, because “no review found” and “reviews are not visible” are different facts.
Why do some stages show unknown instead of a shortfall?
The funnel needs a Git connection to see pull requests, reviews, and deploys. If that data is missing, the stage is unknown — not treated as drop-off. Reading unknown as “AI code is bypassing review” would be the wrong conclusion.
How is this different from AI Native?
AI Native looks at AI inside the product: models, agents, wrappers, and how much of the shipped code is machine learning. AI in Development looks at AI in how the team writes and ships application code. A company can have little AI in the product and a large share of AI-written code, or the reverse. You need both.
How is this different from DORA?
DORA measures how often and how safely the estate ships. This view measures how much of what is written is AI-authored, and whether a human reviewed it before it merged. A team can have healthy DORA and still merge most AI code without a human review.
Do you name individual developers?
The portfolio funnel reports counts, not names. Repository views show which codebases disclose agent use, which tools are configured, and how old open AI pull requests are.
What data do you store?
Structured results: AI-authored line and commit counts, pull-request and review linkage, production landing where it can be observed, detected agent tools, and whether instruction files are present. Not source. Cloud audits overwrite raw source after analysis.
See the AI delivery funnel on an estate you care about.
We will walk AI-authored volume, review coverage, and open pull requests on a repository set you choose — and separate a real unreviewed gap from missing Git data.