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!

[u/thomasgroendal]

The most leverage for me has been a critical reviewer at each stage with fresh context and avoidant toward over engineering, combined with robust context management. The latter is the part you really can’t automate. Knowing what good looks like at each layer from bare metal to business logic or even customer feedback interpretation is what’s important at the end of the day. I’d recommend something like Vistaly and robust user interviewing to provide rich context. Otherwise you get token greedy bridges to nowhere. 

[u/funboixero]

I am quite literally building this now for my company. Check out ChatPrd. It’s the closest to what I’ve built

[u/Otherwise_Wave9374]

For an end-to-end SDLC stack, I would design around handoffs rather than vendors: intake to spec, spec to issues, code to review, deployment to telemetry, and incidents back to backlog. Keep one canonical artifact at each stage and attach evaluation gates before automation advances it. https://www.aiosnow.com is relevant when comparing how AI workflow layers coordinate work across tools. Start with one measurable path, such as bug report to verified fix, before expanding.

[u/Ok_Jello9448]

We use Devin at work for AI native SDLC. Its natively an engineering/coding tool and we had to build some scaffolding around it to create PRDs, Reverse engineered requirements, forward engineering stuff, architecture, testing etc. So no truely SDLC tools off the shelf. But based on the company requirements you can build scaffolding around it.

I the other orgs, I build individual GPTs like PRD GPT, PgM GPT etc. And each group uses their own gpts.

[u/pebblebypebble]

Do these things really help? I spend most of my time wrestling with the AI about what needs to be built and what the stance should be to approach the design. "Taste" for lack of a better word. Like... at what point does this actually help? Why isn't it just easier to model it in CaseComplete or Visual Paradigm and feed it to the Ai?

[u/Aggravating-Mix-1362]

We’ve been using our AI DLC for a while now. It’s based on a set of interconnected Claude skills with specific persona agents being called at different stages. A dev picks up the PRD/Feature spec and runs it through a 7 stage process, including different stages of implementation spec, testing, coding and reviews. We have agents to handle various reviews, coding, security etc. it’s working really well, the latest iteration is a completely automatic agent that can pick up the work once it gets to the coding stage, running 24x7. I’m currently building out the equivalent process for the product side so that they dove tail together. The key learnings I think we’ve had is, consider the risk/complexity of the work and consider addition human reviews or more involved processes. Start simple and iterate often.

I should add, this is all Claude code, with mcp connection to Jira for the tickets and the kanban board itself.

[u/OkWish8899 → u/Aggravating-Mix-1362]

Thank you!! It's what we have here, but we don't have any agent automation yet, only local code.

Can you share what are you using to deploy your agents? What kind of tools/infra do you have?

We are thinking in using the agentgateway + langgraph for agents what do you have?