Venkatesh's Interaction Readiness framework targets AI agents' role failures

Product teams building role-bearing AI agents face a critical blind spot: an agent can be factually accurate, safe, and fluent while still failing the behavioral requirements of its assigned role. Sudhir Alladi Venkatesh's new paper, "Interaction Readiness: A Framework for Building and Evaluating AI Agents in Human Roles" (arXiv:2608.12358), directly addresses this evaluation gap. The framework separates content specifications—what an agent knows and says—from interaction specifications, which define how it should conduct itself in role-governed exchanges. Teams must define role purpose, authority boundaries, recurring situations, boundary cases, repair behaviors, and audit criteria before deployment.

Venkatesh operationalizes interaction readiness through four agent operations: understanding purpose, calibrating authority, managing tone, and repairing breakdowns. Using StudyChat, a public dataset of student interactions with an AI tutoring agent, he demonstrates that content accuracy and interaction quality are independent dimensions—an agent may be factually correct while failing as a tutor, or interactionally sound while technically wrong. The most persistent failure is authority miscalibration: the agent often knows how to answer, but not whether, when, or how its role permits it to answer. The paper translates these findings into a specification template and audit procedures that product and engineering teams can apply before and after deployment, giving them a concrete way to close the gap between knowing and doing in AI agents.