Overview

Every software release carries signals.

Some are easy to see. Others are buried across logs, workflows, support patterns, engineering conversations, and disconnected tools. By the time those signals reach the right people, teams may already be reacting instead of preventing.

This initiative focused how AI-enabled workflows could help Red Hat teams make better pre-release decisions across the Software Development Lifecycle (SDLC).

The goal was to bring scattered operational signals into a clearer experience, one that helped contributors, team leads, and senior leaders detect risk earlier, understand what needed attention, and take action before issues reached customers.

The work supported three outcomes: accelerating adoption, improving operational efficiency, and contributing to a targeted 20% reduction in customer support tickets by helping teams identify and resolve issues earlier in the release process.

Challenge

Signals existed across tools, dashboards, systems, and team workflows, but they were not connected in a way that helped people make confident decisions. Engineering teams had pieces of the picture. Leaders had other pieces. But there was no shared experience that made release health, operational risk, and failure patterns easy to understand.

As AI-enabled predictive failure analysis became part of the broader product strategy, the experience around it needed more structure. Without that structure, AI risked becoming another layer of complexity instead of a source of clarity.

My role

UX Process

Strategy

I approached this challenge through systems thinking and AI adoption, exploring how AI-enabled experiences, agents, and optimization models could help teams solve complex operational problems with more clarity and intelligence.

I mapped the ecosystem around the release process to understand who was involved, what decisions they were making, what tools they relied on, and where context was getting lost.

This helped clarify two key dimensions:

Who the system needed to serve: contributors, team leads, and senior leadership.
How decisions needed to scale: from team-level action to product-level oversight.

This systems view helped create alignment across product, engineering, and leadership before moving into solution design.

Discovery

The workshop alignment clarified two important dimensions: who the system needed to serve (contributors, team leads, senior leadership) and how decisions were expected to scale (team-level execution vs product-level oversight)

Experience delivery Models

To move the work out of abstraction, I used rapid prototyping to make the experience visible, testable, and easier to challenge.

I explored early concepts with Claude Code, shifted into Cursor to accelerate visual iteration, and later aligned back to Claude Code to support engineering consistency and reusable UI patterns.

I also connected prototype logic through MCP servers and an n8n agentic automation flow to simulate more realistic operational conditions. This helped the team evaluate how AI-enabled workflows could surface risk, connect signals, and support action before release.

The prototype became more than a visual artifact. It became a working conversation tool that helped teams understand what the experience could become.

Integration became a key part of the experience strategy because the value of the solution depended not only on interface design, but on how effectively workflows and data could be orchestrated behind it.

• Initial exploration used Claude Code as a baseline
• Shifted to Cursor to accelerate early visual exploration and iteration speed
• After validation, standardized back to Claude Code to align with engineering workflows and ensure consistency across UI patterns and prototype delivery

My team and I evaluated two approaches. n8n provided a flexible model for customizing workflows and pulling data in ways that better supported the intended experience. Grafana offered existing widgets and dashboard capabilities, but it proved limiting when we needed more control over tailored interactions and more flexibility in shaping the overall experience.

Through testing, it became clear that n8n.io provided the stronger path forward. Moving away from Grafana allowed us to establish a more modern and adaptable integration model, with better performance characteristics and greater ability to support the type of experience the organization needed.

Validation

I tested both models through rapid prototyping and stakeholder evaluation. Clear patterns emerged:

• Senior leadership consistently prioritized product-level visibility, efficiency signals, and cross-team comparison
• Contributors and team leads prioritized team-level clarity, consistency, and actionable insights within their workflow

Option A

Option B

Impact