AI-SDLC adoption systems

Problem

Many teams adopt AI coding tools as individual productivity boosters and stop there. That leaves the important problems unsolved: weak acceptance criteria, unreviewed agent changes, unclear ownership, missing verification, and work that cannot be resumed after the chat context disappears.

What I built

I build AI-SDLC adoption systems that turn agentic coding into a repeatable engineering workflow. The pattern combines clear task framing, scoped file ownership, evidence-first handoffs, code review habits, verification loops, and GitOps or task-json rails for durable execution state.

The work is intentionally practical. It is less about showcasing a single impressive prompt and more about making AI engineering safe enough to use on real codebases with other humans and agents present.

How I work

I separate the workflow into explicit stages: recover the goal, challenge weak assumptions, define the next verification gate, make the smallest useful change, inspect the diff, and leave a record that another operator can use.

When the same defect class survives repeated fixes, I do not keep patching blindly. I raise the verification level: from static inspection to focused tests, then integration or operator-level checks when the risk calls for it.

What this demonstrates

This work demonstrates AI Integration, developer productivity, and senior engineering judgment: building agentic workflows that are observable, bounded, reviewable, and compatible with normal software delivery discipline.