Building Agents That Get Used: The Trust Ladder from Autocomplete to Autonomous
The biggest barrier to AI agent adoption is trust, not technology. This talk introduces the "trust ladder" framework, showing how the most successful AI tools earn user confidence by starting small and expanding autonomy over time. Drawing on case studies from coding IDEs, writing assistants, and notable failures, attendees will gain a practical model for evaluating agent readiness, understanding why progressive autonomy outperforms big-bang deployment, and applying verification-based workflows to autonomous systems in their organizations.
Russell Cates is an MIS graduate student at Texas A&M University's Mays Business School, an Applied AI Fellow at Texas A&M where he teaches faculty and students how to effectively use AI in their work, and an incoming Production Operations Engineering intern at Meta. He leads Aggie Innovators, a student organization focused on practical AI adoption, and has led workshops on tools like Cursor and Claude Code. Since late 2022, Russell has worked extensively with emerging AI tools across a range of frameworks and domains. Through conversations with industry practitioners at companies both succeeding and struggling with AI agent adoption, he has developed a grounded perspective on what drives trust in AI tooling.
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