Morgan Joyce

About Morgan Joyce

I build AI control planes and agent systems that turn constraints into leverage. Parallel AI orchestration is my default operating mode (tmux + git worktrees + multi-agent pipelines), which is how I’ve delivered throughput gains without adding complexity.Now at Wolters Kluwer (DXG FCC CT), I’m focused on FAB—the internal GenAI platform that governs how every product ships agents safely. It’s a multi-cloud control plane (AWS + Azure) with gateways, guardrails, RAG pipelines, datasets, evaluations, and product-facing agent surfaces in real legal/compliance products. My role is to make agentic capabilities production-ready: governed, observable, and embedded directly into products customers use.Previous examples of how I work:Provider-agnostic standards across Copilot, Claude Code, and Cursor to prevent vendor lock-in and accelerate reviewsEliminated a 136-line permission system down to 2 flags—subtraction as a performance strategy4,400+ commits orchestrating agents and MCP servers to turn sequential work into parallel executionI don’t chase hype; I ship governed agent systems in regulated environments. If you’re building production agentic AI—or want to compare notes on control planes, gateways, and evaluation loops—let’s connect.

Experience

1. Senior Software Product Engineer, Agentic Ai (Fab)

   Wolters Kluwer

   Nov 2025 — Present · Austin, TX, US

   Build FAB control-plane primitives (workspaces, gateways, routing) across AWS/Azure with strict isolation.Embed agents into customer-facing legal/compliance products and enterprise surfaces (Teams, SharePoint, UIs, APIs) with guardrails, auditability, and policy-backed handoffs.Stand up dataset → Tune → Index → product experience loops with LLM-judge scoring, RAG Checker, and telemetry to measure production agent quality.

Education

• New Trier Township High School Winnetka

  High School Diploma

• Oakton College

  Associate of Arts - AA

• Udacity

  Programming for Data Science with Python, Computer Software Engineering