Most AI-assisted development workflows have a black box problem — agents generate code, specs, and decisions, but the artifact trail evaporates. When something breaks (or a stakeholder asks "why did it do that?"), there's nothing to point to.

This session walks through a spec-driven, agentic SDLC we built and battle-tested in a live community build cohort (Atlanta Tech Community Network, Cohort #1). Every stage — PRD → technical spec → implementation → QA → DevOps — is gated with a "works cited" checkpoint: structured artifacts that show what the agents produced, what sources they used, and what decisions were made before the next stage opened.

You'll leave with:
- A blueprint for a multi-agent pipeline with clear handoff gates
- The "works cited" pattern for AI artifact provenance
- Lessons from running this as a teaching vehicle with real contributors and a real product shipping real code

(This is the system that I built for my current job (Connexure) and I teach as part of Atlanta Tech Community Network)