Atriumn AI SDLC
Structured AI workflows for GitHub. Context included.
AI is great at spitting out code. It's also great at losing the plot.
Without structure, you get walls of text and random diffs instead of useful work.
Atriumn SDLC is an opinionated way to run AI inside your software lifecycle. It keeps context tight, breaks work into phases, and records everything in the repo — so you can actually trust what it's doing.
Pieces You Can Use
Workflow
Each issue flows through the same phases: Research → Plan → Implement → Validate. AI runs in CI on a feature branch. At the end of each phase, you approve before it moves forward.
- Every task starts with a GitHub issue. That's the single source of truth.
- One draft PR per issue tracks the whole lifecycle. The checklist updates as phases complete.
- Research notes, plans, and generated code all live in Markdown or commits in your repo. No mystery files, no disappearing context.
Under the Hood
- GitHub App adds slash commands (/atriumn-research, /atriumn-plan, etc.).
- GitHub Actions run Claude Code with strict prompts.
- Shared workflows handle orchestration.
- MCP (Model Context Protocol) makes the same task packs usable outside GitHub — in Claude Desktop, Claude Code, or your own app.
Why This Matters
Managing context is everything. Anthropic pointed this out: once you lose context, the outputs fall apart.
Atriumn SDLC bakes context management into the process. Every artifact is checked in. Every step is explicit. The AI doesn't "forget" what phase it's in — and neither do you.
Ready to Structure Your AI Workflows?
Interested in implementing structured AI workflows in your development process? Let's discuss how Atriumn AI SDLC can fit into your team's workflow.