Kishore's AI-SDLC Experiences: From Backlog Refinement to Implementation Readiness

Vision: Use AI-SDLC Mob Programming playbooks for implementation-ready outcomes

Author

Aruna Kishore Veleti
June 10, 2026

Kishore's AI-SDLC Experiences: From Backlog Refinement to Implementation Readiness

Welcome to AI-SDLC “Mob Programming”

I recently published a GitHub repository: AI-SDLC-Mob-Programming-Testing

The repository introduces a practical approach for applying AI-SDLC Mob Programming across software delivery initiatives.

Many teams already use GitHub Copilot, ChatGPT, Gemini, Claude, and other AI tools. The challenge is no longer access to AI.

The challenge is creating a repeatable framework that helps teams collaborate effectively across requirements, architecture, development, testing, security, and operations.

This repository focuses on that problem.

Traditional Backlog Refinement vs AI-SDLC Mob Programming

Traditional backlog refinement typically answers: 

What work should we put into the sprint?

AI-SDLC Mob Programming asks: 

What do we need to collectively understand,
design, test, secure, operate,
and implement before the work starts?

The outcome is not just a user story.

The outcome is implementation readiness.

SKILL.md Playbooks

The repository contains reusable SKILL.md playbooks that help teams run structured AI-assisted mob programming sessions.

Each playbook defines:

  • Required tools

  • Required access

  • Required system scopes

  • Ideal participant roles

  • Session structure

  • Expected outcomes

  • Deliverables

  • Testing strategy

Shopify Middleware Example

The first implementation domain is Shopify Headless Middleware.

One example focuses on Discount Segmentation.

The playbook helps teams collaboratively define:

  • Guest customer discounts

  • Logged-in customer discounts

  • VIP segmentation

  • Wholesale pricing

  • Middleware APIs

  • Shopify dependencies

  • Test scenarios

  • Edge cases

Instead of creating meeting notes, the goal is to produce implementation-ready artifacts.

Current Playbooks

  • Discount Segmentation

  • Checkout Customization

  • Inventory Synchronization

  • OMS / ERP Integration

Shopify is the first domain implementation.

The framework itself can be applied to enterprise integrations, cloud-native systems, microservices, AI agents, data platforms, quality engineering, and DevOps initiatives.

The vision is simple:

Use AI-SDLC Mob Programming playbooks to move teams from backlog discussions to implementation-ready outcomes.

Read more in my blog post

AI-SDLC Experiences: AI-SDLC Mob Programming in Action - Product Cognizant

Welcome to AI-SDLC “Mob Programming” I recently created a repository called: AI-SDLC-Mob-Programming-Testing The goal is to create a reusable collection of AI-SDLC playbooks that help teams collaborate more effectively and produce implementation-ready outcomes faster. Repository:

Product Cognizant • Kishore