For enterprise implementations running multiple workstreams AI-assisted delivery can create efficiencies, but there’s a real challenge: moving faster while staying together.
This client wanted that answer proven, not promised. Their portfolio provided the right conditions to test it: enough ticket volume to generate meaningful data, enough brand variation to require shared standards rather than one-off fixes, and a live production environment where the playbook had to perform at scale.
The ambition was to give tech leads and engineers a way to use AI tooling inside the actual pipeline (ticketing, design handoff, code standards, PR review), and prove that it could support the demands of a multi-brand enterprise portfolio.
From prompting to process
Before this initiative, the client’s engineers were using Cursor and Copilot much like most teams: individually and without a shared process. One engineer might prompt for accessibility compliance while another approached the same task differently. Both could get useful results, but without consistent guardrails or documentation, those differences quickly multiplied across a portfolio of brands running on the same platform.
Domaine’s Agentic Assisted Development Playbook replaces that with something versioned and shared: a workflow that lives directly in the repository, covering technical approach, development, QA, test steps, and PR creation, with Cursor as the primary tool for engineers and tech leads. Every engineer now works from the same process and standards, which matters in a multi-brand environment where the same component can ship differently from one storefront to the next.
The context layer is the real build
Jira, Figma, Shopify and browser tooling are wired directly into the dev loop, so agents pull live ticket details and design tokens rather than working off whatever a developer pasted in. Alongside it sits a maintained ruleset carrying our Liquid, CSS, accessibility and multi-brand standards. the judgment calls that normally live in a senior engineer's head, made available to every PR across every brand.
That context layer does several jobs at once:
- Pulls ticket detail and acceptance criteria directly from Jira, so the agent works from the requirement itself, not a paraphrase
- Reads design tokens and component specs straight from Figma, keeping implementation aligned to the live source file
- Checks output against a live Shopify environment before a developer does
- Applies the maintained ruleset of Liquid, CSS, accessibility, and multi-brand conventions to every suggestion, so standards don't depend on which engineer is at the keyboard that day
Measuring more than speed
Feature tickets built through the playbook landed roughly 50% faster than estimated for the client’s engineering team. But velocity was only one measure of the workflow’s effectiveness. During the same period, unbudgeted bug work accounted for close to a quarter of ticketed engineering time, highlighting where faster development was also creating rework.
By tracking both across the same tickets, the client’s engineering team could identify where the workflow was delivering efficiency gains and where the process still needed improving. This gave them a more complete picture of AI-assisted development: not just how quickly work could ship, but where those gains translated into overall engineering efficiency.
A better agent of change
We ran the retro on that bug volume the way we'd run one on any process failure, and rebuilt the workflow around what it found. The changes now apply across every brand in the client's portfolio:
- A two-part technical approach that separates planning from implementation, so an agent isn't reasoning about architecture and syntax in the same pass
- A pre-PR closeout step and automated code review on every PR, so the errors that used to surface in QA get caught before a human reviewer sees the pull request
- Test steps written in plain, human-readable language that any reviewer can follow
- Model-to-complexity standards, so a routine styling fix and a checkout-flow change aren't handled by the same tier of model
That sequence, measuring where rework was happening and rebuilding the process around it, is what makes the workflow more robust. Many teams experimenting with AI-assisted development don’t have the data to understand where it’s creating additional work. By tracking it closely, we could identify the patterns, address them, and build those learnings back into the process across every brand.
Human by design
Governance is scrutinized as closely as velocity, so human accountability was built into the workflow from the start. Every bug fix goes through human triage before an agent touches it, with engineers remaining accountable for everything that ships under their name.
Discipline expertise is protected in the same way. Accessibility standards, Liquid conventions, and multi-brand rules capture the knowledge of senior engineers and make it available across every PR, allowing expertise to scale rather than remain with individuals.
For enterprise teams, this addresses one of the biggest questions around agentic development: who is accountable when an agent gets it wrong, and how do you scale AI without diluting human expertise? Building those safeguards into the workflow gave the client the confidence to adopt it at scale.
Ready to write your own playbook?
Governance, not the velocity number alone, is what makes agentic development trustworthy at enterprise scale. This holds across ecommerce, whatever the portfolio looks like. A single-brand retailer scaling fast, a health and beauty group managing dozens of SKUs across regions, and a multi-brand portfolio like this one all face the same shape of problem: fast AI-assisted output that still has to answer for itself.
This client wanted evidence before they'd trust AI tooling inside their delivery pipeline, and now they have it. Whether you're running one brand or several, we're glad to walk you through how we structured it. Get in touch with our AI team today.
