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