The AI-native SDLC
AI agents run your SDLC as a production line.
Tickets in, reviewed pull requests out. Agents plan, implement, test and review every change. Your engineers sign off each stage and own the merge.
- 01Planan agent drafts the spec; an engineer signs it off
- 02Buildagents implement in an isolated sandbox
- 03Testautomated quality checks on every change
- 04ReviewAI review, tiered findings on the pull request
- 05Releasemerged by a person, never an agent
One change, end to end, with your engineers at the controls.
Plan, build, test, review, release — the stages your team already runs, executed by agents and signed off by people. Then watched after the merge.
- 01
A ticket starts the line
Label an issue in GitHub, Jira or Linear — or mention the app — and a change opens against it. It is triaged first: trivial, needs a plan, needs design, or a question back to you on the ticket. Parked work gets a date to come back.
agent - 02
Agents write the plan first
Research, a design and a plan written as small tasks, as documents your engineers read and edit. Nothing is coded without a plan, and a person signs it off — except a trivial, low-risk change once the automation has earned that.
agent drafts · engineer signs off - 03
Implementation in a sandbox, on a budget
Each change runs in its own isolated environment with a budget for tokens, time and lines. It pushes a branch and opens the pull request under the app’s identity.
agent - 04
Quality checks, then an AI review
Tests, your own CI, secret scan, static analysis, dependency and architecture policy, a test-integrity check, and acceptance scenarios the agent never saw — then a review whose findings are tiered, with only the act-on tier sent back. Three rounds at most; then it asks a person.
agent - 05
Your engineer merges
You see the change once, after every machine check has passed, with its audit trail: the plan, the checks, the findings, the spend. A person decides; the agent never merges its own work.
your engineer - 06
After the merge, it keeps watching
Your default branch, reverts and error alerts are watched after a merge. A severe regression gets a revert pull request a person merges; anything else becomes a ticket, deduplicated, back through triage.
agent
Budgets
Every change has a budget. Nothing spends unbounded.
Budgets are set in dollars — for the organisation, per member, and on every change — with limits on time and size beside them. A run that reaches its limit stops and asks. The spend is on the pull request and on the invoice, change by change.
Limit reached → the run pauses and a person decides: raise it, split it, or stop.
- Lowautomated checks · AI review
- Medium+ plan sign-off by an engineer
- High+ architecture policy · second review
- Critical+ named approver from the owning team · attended run
Change risk
The change decides how much oversight it gets.
Services touched, the kind of work, the paths under policy, the reach of the change — recorded signals set a risk level, and the level decides which checks run and who signs off. Nobody sets it in a settings screen, and a missing signal raises it, never lowers it.
Before an agent touches a file it reads what governs it — your architecture decisions, the patterns the team settled on, the gotchas nobody wrote down until now, and who owns the code — from a knowledge graph built from your repository, and kept current as it changes.
It never merges its own pull request.
Your engineer does — with the plan, the checks and the spend in front of them.
Autonomy
Unattended is earned, not switched on.
- AttendedA new automation runs with a person watching every stage.
- SupervisedAfter clean runs, sign-offs move to the points the risk level requires.
- UnattendedEarned per automation and per risk level by its own record. There is no switch.
- ImprovingRuns are scored daily. A proposed fix is replayed against the runs that failed before you see it, and rolled back on its own if it does not hold.
Works with GitHub, Jira, Linear, Slack and any MCP-capable editor. See every integration →
Run one change through the line this week.
Connect a repo and a tracker, label a ticket, sign off the plan. The pull request arrives with its audit trail.