Context
The platform is designed for enterprise prototyping where AI-assisted implementation must remain inside delivery controls. Claude Code is an execution component; the operating model is the more important contribution.
Workflow
Intake and requirements decomposition - solution design - implementation plan - AI-assisted execution - pull request - automated tests - human review - security review - release evidence - retrospective and evaluation.
Azure DevOps owns work items, acceptance criteria, repositories, pull requests, pipeline evidence, approvals, and release history.
Governance artifacts
- Requirement traceability and design rationale.
- Session summary, generated diff, and test evidence.
- Risk flags, security considerations, review notes, and approval history.
- Deployment-readiness checklist and known limitations.
Measurement layer
The evaluation design measures whether output met acceptance criteria, passed tests, followed repository conventions, introduced security concerns, or required significant human rework.
Useful operating metrics include cycle time from idea to pull request, acceptance rate, test-coverage delta, review rework, security findings, cost per prototype, and developer-reported time saved.
This approach connects delivery evidence to the public LLM Evaluation Workbench methodology.
Security boundaries
The public pattern emphasizes scoped repository access, secret exclusion, least-privilege tool permissions, human code review, retained evidence, approval gates, and escalation of uncertain outputs.
This case study intentionally excludes confidential client details, internal configuration, and unvalidated impact claims.