I built an 8-agent AI pipeline that keeps full context across every step — here's the architecture

The problem with most AI coding tools: every prompt starts from zero. By step 5, the AI has forgotten what you decided in step 1.
So I built a pipeline where that can't happen.
How it works
Eight specialized agents run sequentially. Each reads a shared context object containing every decision made by every prior agent.
Analyst → Architect → Orchestrator → Engineer → Reviewer → QA → Release Manager → Release
No phase begins until the previous one passes a gate check. If QA finds a bug, it routes back to the Engineer with the full original spec intact.
The shared context object
yamlcontext:
problem:
statement: ""
success_criteria: []
constraints: []
solution:
approach: ""
tech_stack: {}
code:
files: []
review:
approved: false
tests:
passed: false
Every agent reads and writes here. The Reviewer checks code against original requirements. QA validates against Phase 1 success criteria — not just whether the code runs.
What I learned
The hard part isn't the agents. It's the gates. Bad output that passes unchecked compounds fast across 8 phases. Every transition needs an explicit pass/fail.
Try it
Live demo, free, no signup: friendly-maamoul-bf2dd5.netlify.app
What would you architect differently?