From Coding Assistants to Governed Delivery Systems
Most AI-in-engineering content tells you what's possible. This paper tells you what it cost.
Coding assistants gave our teams real gains — then a ceiling. Wiring agents into tickets and pipelines pushed further, and hit another one. What finally moved the needle wasn't a better model. It was making the delivery process itself executable: durable workflows that survive waiting, failure and restart; human approval at the points where accountability actually sits; and a system that turns every review comment into reusable engineering knowledge.
Inside, first-hand:
The three grades of AI adoption in an SDLC — and the specific ceiling each one hits
How the architecture emerged phase by phase, each step forced by the previous constraint
What it really takes to stand a factory line up — the part nobody budgets for
Why a misaligned artifact is an error multiplier when the multiplier is throughput
15 lessons, and 7 risks stated with the point where each mitigation stops working
What we've proven — and what we haven't
Everything runs inside your environment. Your code, telemetry and knowledge base never leave your estate.
No empty 10x promises. No manufactured ROI. This is the real story of adopting into the software delivery lifecycle—the wins, the setbacks, and the lessons that matter.
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