AI-native engineering: how IT and software teams build with agents
Engineering
Native helps engineering and IT teams work AI native: coding agents across the delivery cycle, AI code review, legacy migration and internal tools.
Where AI has an impact in engineering and IT
AI code review
Every pull request reviewed by an agent first, with findings written up for the engineer.
Spec to pull request
Small changes and fixes drafted from a ticket as a reviewable pull request.
Test generation
Tests written and maintained alongside the code they cover.
Legacy migration
Old systems read, mapped and moved piece by piece, with behaviour proven unchanged.
- Internal tools
- Documentation
- Incident triage
- IT service desk
AI-native engineering means agents write, test, review and document code, and engineers set the direction and make the calls. Native helps IT and software teams make that shift on their own codebases, starting with one workflow in production within weeks. It is also how Native builds every system it delivers.
Case study · A mid-market software company
A mid-market software company moved its engineering team to an AI-native workflow.
Native helped a software company's engineering team put agents on code review and its backlog of small changes. Every pull request now gets an AI review first, and most small fixes arrive as draft pull requests for an engineer to approve.
The challenge
A small team was split between roadmap work and a growing queue of small requests from the business. Reviews were the bottleneck, and minor fixes waited weeks.What we built
- AI review on every pull request, with findings posted for the author before a teammate looks.
- An agent that turns small-change tickets into draft pull requests with tests, for an engineer to review and merge.
- Written practices for working with agents, so new engineers adopt them from their first day.
The result
The backlog of small changes cleared, reviews stopped holding up releases, and the team spent its time on the roadmap.Frequently asked questions
What is AI-native engineering?
It is a way of building software where agents write, test, review and document code, and engineers set direction, review the work and make the decisions. It goes beyond coding assistants in an editor to agents across the whole delivery cycle.
Will AI agents merge code on their own?
No. Agents open draft pull requests; a person reviews and merges. Branch protection and test gates stay in place.
Where should an engineering team start with AI?
AI code review on every pull request, or a queue of small changes worked by agents. Both are quick to set up and show their impact within weeks.
Can AI help with legacy systems?
Yes. Agents read old code, map what it does, and help migrate it piece by piece, with tests proving the behaviour is unchanged.
How do you measure AI's impact on engineering?
Lead time, the share of changes drafted by agents and merged, review findings caught early, and escaped defects. Not lines of code.
