Week 01
Map & measure
Follow the delivery workflow. Establish baseline metrics and identify the main constraints.
Assessment & Transformation Sprint
AI-Native SDLC.
A working start.
Find where AI can improve your delivery process, put the best opportunities into practice, and give leadership evidence for what to do next.
Who it is for
You have AI tools.
You need a clearer return.
The sprint is for engineering leaders seeing uneven adoption, growing review queues or uncertainty about agent permissions and quality.
We focus on one pilot team and selected repositories. An executive sponsor, access to delivery data and participation from security help us turn the assessment into working changes.
A bounded starting point for organizations with roughly 50–500 software engineers.
What you receive
An assessment.
A working pilot.
A plan to scale.
Each deliverable supports a concrete decision about how your engineers should work with AI.
- 01Current-state assessment
Lifecycle map, maturity assessment and prioritized opportunities. - 02Agent and context design
Recommended tool architecture, repository conventions and knowledge access. - 03Control and responsibility model
Agent permissions, human decisions and quality gates. - 04One or two implemented workflows
Bounded pilots in your development environment. - 05Measurement and 90-day roadmap
A baseline scorecard, pilot findings and a sequenced implementation plan.
Inside the engagement
Assessment becomes
implementation.
A typical four-week sequence. We agree the exact scope and timing up front based on access, complexity and team availability.
Week 02
Design & align
Select pilot workflows. Agree agent roles, permissions, checks and acceptance criteria.
Week 03
Implement & coach
Put the workflows into practice with the pilot team. Capture effort, feedback and exceptions.
Week 04
Evaluate & plan
Review evidence against the baseline. Decide what to expand and sequence the next 90 days.
Success means better
delivery economics.
We agree targets after discovery and compare similar work. Faster delivery must hold up alongside quality and total cost.
| What we measure | Example pilot criterion | Evidence |
|---|---|---|
| Delivery cycle time | 20% lower than baseline | Issue and deployment records |
| PR review wait | 25% lower than baseline | Pull request timestamps |
| Change failure rate | No deterioration | Deployment and incident records |
| Cost per accepted feature | 10% lower than baseline | Labor, rework, AI and rollout costs |
Illustrative criteria, not promised results. Final targets depend on the baseline and scope. Recovered capacity creates value when the team puts it to productive use.
After the sprint
A clear decision
about what comes next.
If the pilot meets the agreed criteria, the AI Engineering Operating System engagement embeds the practices across selected teams over 8–16 weeks.
You can also use the roadmap with your own team. The sprint stands on its own, with working workflows and an actionable implementation plan.
The first conversation
One workflow worth improving.
A 30-minute discussion with your engineering sponsor and a pilot lead is enough to establish the starting point.