AI develops under governance. People approve.
Turn GitHub issues into production-ready pull requests through spec-driven development, independent verification and confidence scoring, with a human approval at every critical gate. Runs on the AgenticOps control plane.
AI coding accelerates development. It also propagates errors faster.
Today's AI coding pipeline is optimized for velocity. When an error enters early, every stage builds on it: an incomplete requirement becomes a flawed design, a broken plan and a wrong implementation.
The tests don't catch it, because they were derived from the same flawed work. They validate the wrong thing, and the mistake reaches production with high impact.
Generating code is easy. Trusting it in production isn't.
Identify gaps and uncertainty early.
Every stage ends at a confidence and approval gate. Low confidence sends the work back to the stage that produced it, before anything else is built on top.
In this example, an incomplete requirement is caught at the Spec & Plan gate instead of after merge. Failed tests send work back to code generation, and nothing reaches merge without a final readiness approval.
Confidence is established at every stage.
Each stage is scored against an explicit rubric. A stage's confidence combines its own rubric score with the confidence of the stage before it, so uncertainty carries forward instead of disappearing. Every score leads to a decision: proceed, or rework.
- Completeness
- Clarity
- Trackability
- Assumptions
- Scope & Risk
- Testability
- Spec Traceability
- Tech Soundness
- Task Completeness
- Risk & Rollback
- TDD Strategy
- Spec Compliance
- Plan Compliance
- Security
- Coverage
- Functional Correctness
- Test Pass Rate
- Test Coverage
- Test Execution Integrity
- Verification Completeness
Confidence cascades left to right: a weak specification lowers the confidence of everything built on it, even when later rubric scores look good.
What makes it governed.
Issue analysis & specification
GitHub issues become implementation-ready specifications: business objective, scope, acceptance criteria, verification plan and dependencies.
Ambiguity detection
Missing information is caught before implementation begins, with clarification questions generated automatically to remove assumptions.
Confidence-driven development
Confidence scores across the SDLC give reviewers visibility into AI-generated artifacts before they enter the enterprise pipeline.
Independent AI verification
Separate Builder and Tester agents, specification-derived tests and black-box validation reduce implementation bias.
Human-governed development
AI performs the implementation while people keep control over intent, scope, risk, quality and production readiness at every critical decision point.
Enterprise CI/CD integration
Approved pull requests flow directly into your existing pipelines, adding governance without disrupting release processes.
From "AI writes code" to "AI develops under governance".
| Traditional AI coding | AI Governed SDLC |
|---|---|
| Starts from an issue | Starts from a validated specification |
| AI writes code | AI develops under governance |
| Tests often derived from the implementation | Tests derived from the specification |
| Limited visibility into trust | Confidence scores throughout the SDLC |
| Focus on code generation | Focus on trusted software delivery |
What teams get.
Better requirements
Move from issue-driven to specification-driven development.
Higher quality
Independent verification reduces implementation bias and improves test quality.
Enterprise governance
Human approvals stay at critical decision points while AI automates implementation.
Reduced review effort
Confidence scores point reviewers at the high-risk changes.
Faster delivery
Repetitive development work is automated while enterprise controls stay in place.
Trusted AI adoption
A defensible path to deploying AI-generated software into production.
People on the loop, in practice
This is the vision applied to software delivery: AI does the implementation while people set intent, own the gates and judge outcomes. Every approval, rework decision and rubric score becomes Operational Intelligence for the next issue.