End-To-End AI QA Pipelines with Claude Code + Mcp: Requirements → Jira → Test Cases → Mobile Automation at ~80% Accuracy

At Motorola Solutions and previously Match Group / Tinder, I redesigned the QA pipeline around Claude Code and the Model Context Protocol. The system ingests raw feature requirements, maps them into Jira stories, generates functional and edge-case QA test cases, and produces executable mobile automation — all with MCP servers hooked to Jira, the test-case repository, and the automation codebase. It currently runs at ~80% accuracy on automated flow generation.

Structurally, this replaced a manual three-hop pipeline (PM writes spec → QA reads spec → SDET writes automation) with an agent-orchestrated one where deterministic tools still handle the hard edges (auth, flakiness detection, diff review) and the model handles translation and scaffolding.

Attendees will walk away with: (1) a reference architecture they can lift, (2) concrete integration patterns for MCP ↔ Jira ↔ automation repo, (3) honest cost math — engineering hours, token spend, CI minutes — for getting from prototype to production, (4) a failure-mode catalog with the guardrails that caught silent regressions, and (5) a 30/60/90-day adoption plan for their own QA orgs.