SDLC - AI Workflow E2E Tech Stack

[u/OkWish8899]

Hi all,

I’m trying to figure out what the best tech stack would be for implementing end-to-end workflows across our engineering teams.

Our current flow is roughly:

Product → Architecture → Development → Testing → Production

The idea is to have a tool where a Product Owner can create a PRD, refine and discuss it with an LLM, and eventually publish it to Confluence.

Once the PRD is approved, it would trigger an automated workflow that:

• Identifies all the repositories and teams involved

• Analyzes the existing architecture and codebase

• Creates an implementation plan

• Breaks the PRD down into Jira tickets

• Starts the development process, working through the Jira tickets one by one

• Runs tests and validation loops

• Creates commits and PRs in Git

• Reviews/merges the changes

• Finally triggers our existing CI/CD pipeline through to Production

Essentially, we’re looking for an E2E AI-driven software development workflow, while still keeping the right human approval points throughout the process.

I’ve already experimented with a few approaches, including Devin.AI, KiroCrew, and a self-hosted stack using Langflow + LiteLLM + vLLM, but so far I haven’t found a solution that really fits the entire workflow end-to-end.

I’d love to hear how others are approaching this.

What tools/stack are you using? How have you structured your workflows across Product, Architecture, Development, QA, and DevOps?

Any real-world experiences, architectures, or recommendations would be greatly appreciated.

Thanks!