175 - The MIRRR UX Framework for Designing Trustworthy Agentic AI Applications (Part 1)

Experiencing Data w/ Brian T. O’Neill (AI & data product management leadership—powered by UX design)

Content provided by Brian T. O’Neill from Designing for Analytics. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Brian T. O’Neill from Designing for Analytics or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

4M ago 28:51

MP3•Episode home

In this episode of Experiencing Data, I introduce part 1 of my new MIRRR UX framework for designing trustworthy agentic AI applications—you know, the kind that might actually get used and have the opportunity to create the desired business value everyone seeks! One of the biggest challenges with both traditional analytics, ML, and now, LLM-driven AI agents, is getting end users and stakeholders to trust and utilize these data products—especially if we’re asking humans in the loop to make changes to their behavior or ways of working.

In this episode, I challenge the idea that software UIs will vanish with the rise of AI-based automation. In fact, the MIRRR framework is based on the idea that AI agents should be “in the human loop,” and a control surface (user interface) may in many situations be essential to ensure any automated workers engender trust with their human overlords.

By properly considering the control and oversight that end users and stakeholders need, you can enable the business value and UX outcomes that your paying customers, stakeholders, and application users seek from agentic AI.

Using use cases from insurance claims processing, in this episode, I introduce the first two of five control points in the MIRRR framework—Monitor and Interrupt. These control points represent core actions that define how AI agents often should operate and interact within human systems:

Monitor – enabling appropriate transparency into AI agent behavior and performance
Interrupt – designing both manual and automated pausing mechanisms to ensure human oversight remains possible when needed

…and in a couple weeks, stay tuned for part 2 where I’ll wrap up this first version of my MIRRR framework.

Highlights / Skip to:

00:34 Introducing the MIRRR UX Framework for designing trustworthy agentic AI Applications.
01:27 The importance of trust in AI systems and how it is linked to user adoption
03:06 Cultural shifts, AI hype, and growing AI skepticism
04:13 Human centered design practices for agentic AI
06:48 I discuss how understanding your users’ needs does not change with agentic AI, and that trust in agentic applications has direct ties to user adoption and value creation
11:32 Measuring success of agentic applications with UX outcomes
15:26 Introducing the first two of five MIRRR framework control points:
- 16:29 M is for Monitor; understanding the agent’s “performance,” and the right
  level of transparency end users need, from individual tasks to aggregate views
- 20:29 I is for Interrupt; when and why users may need to stop the agent—and
  what happens next
28:02 Conclusion and next steps

109 episodes

#Tech #MBA #Business #Brian T. O’Neill from Designing for Analytics #Arts #Graphic Design #Data Product