@misc{f67bec93260f45d2a852a80619782aea,
title = "What Is the Agent Doing? Visualizing Agentic AI Querying Workflows",
abstract = "We explore how visualizations can help users understand what an AI agent is doing as it builds and runs queries over data. As part of the LinkQ system, a natural language interface for querying knowledge graphs with a large language model (LLM), we designed two complementary views: A State Diagram that shows where the agent is within a larger workflow, and a Live Action Display that gives real-time updates about the agent's current task. In a study with 14 practitioners, we found that these visuals helped participants build stronger mental models of the agent's behavior while also increasing their confidence in the system. However, we also observed that users sometimes trusted incorrect outputs simply because the agent appeared to be doing the {"}right{"} thing. Our findings point to both the value and risk of visualizing agent behavior in interactive AI systems.",
keywords = "explainable AI, knowledge graphs, large language models, natural language interface, query builder, trustworthy design",
author = "Harry Li and Gabriel Appleby and Kenneth Alperin and Steven Gomez and Ashley Suh",
year = "2025",
doi = "10.2172/3031254",
language = "American English",
series = "Presented at the IEEE VIS x GenAI Workshop, 3 November 2025, Vienna, Austria",
publisher = "National Laboratory of the Rockies (NLR)",
address = "United States",
type = "Other",
}