This case study showcases the design work I led and contributed to (2025-2026), for producing net-new, redesigned Xfinity member-facing experiences leveraging LLM technology and agentic workflows. This work is set to impact millions of Comcast Xfinity customers by the end of 2026.
1. Agentic Outage Experience
I led the design of the prompt framework and proof-of-concept demo for the 2026 next-generation outage solution, pioneering agentic and contextually intelligent messaging. The demo demonstrated three new features during a complex event where a planned network maintenance window overlapped with an unplanned severe-weather incident.
Xfinity climbed from last place in 2022 to first place in timely, reliable proactive outage communications in 2025 — J.D. Power.
The Problem: Today’s messaging experience for outages doesn’t consistently do the following:
A) Provide members with a unified experience across outage types (planned/ unplanned) or channels (proactive/reactive).
B) Deliver critical updates in a timely manner.
C) Assist with outage-related member inquiries.
This can result in a member experience that feels robotic and increases members' effort when contacting support during an already frustrating event. This reduces members' confidence in Xfinity’s automated service comms.
Goal: Deliver a unified, proactive, effortless messaging experience that drives intelligent conversations with members impacted by 3 or more planned or unplanned outages within a month.
My Role: Designed reusable prompt framework, conversational script, agentic interaction, and demo.
Existing outage notifications bombard members (customers) with status updates almost daily or every few days as the problem code/job type changes from planned to unplanned outage.
Initial concept exploration
The minimum viable experience demonstrated three capabilities: contextually aware responses, natural-language utterances with turn-taking, and rich business messaging.
Vision and ideal-state experience
The initial exploration established the experience vision before LLM resources and technology became available for use.
Targeted areas of improvement
The prompt framework focused on stronger context retention, more effective use of telemetry, clearer response structure, reduced repetition, natural turn-taking and graceful escalation when automation could not resolve the issue.
