Information Seeking in the Age of Agentic AI

Overview

This tutorial introduces a practical framework for information seeking in the age of agentic AI. It focuses on how information needs vary in complexity, how that complexity shapes the need for planning, tool use, and grounding, and when different paradigms such as search, LLMs, and agentic workflows are most appropriate. Through interactive and hands-on implementation activities, attendees will analyze tasks and build workflows for structured lookup, grounded retrieval, live web search, and agentic navigation.

Important links

• Activity A: Complexity Ladder (Take me there)
• Activity B: Structured Lookup & Grounded Closed-Corpus (Take me there)
• Activity C: Grounded Live-Corpus (Take me there)
• Activity D: Agentic Navigation (Take me there)

Materials

Slides and demo notebooks are available below.

Intended Audience and Prerequisites

Researchers, students, and practitioners in IR, HCI for IR, evaluation, and applied ML who are interested in user-centered study of agentic information access. Familiarity with core IR concepts is assumed. No prior experience with agentic frameworks is required.

Learning Outcomes

The tutorial is designed for practitioners and researchers interested in understanding how to leverage and evaluate agentic systems for information seeking. It is relevant to all conference attendees, including students, early-career, and experienced researchers.

This tutorial will provide attendees with:

• A conceptual framework for classifying information-seeking queries by complexity — from simple re-expression to multi-step agentic navigation;
• Hands-on experience grounding LLM outputs with structured APIs, closed corpora (RAG), and live web search, and understanding the trade-offs between each;
• Practical exposure to the ReAct framework for building agents that navigate multi-step information tasks beyond what a single search query can reach;
• Intuition for evaluating agentic systems: when grounding succeeds, when it fails, and how retrieval strategy affects answer quality.

Schedule

Presenters

Preetam Dammu

University of Washington, Information School

Email: preetams@uw.edu

Preetam Dammu is a Ph.D. candidate in Information Science at the University of Washington. He works at the intersection of Information Retrieval and Generative AI, studying how people and AI systems seek, verify, and use information in dynamic, open-world environments. His current research focuses on making information-seeking agents and retrieval-augmented systems more reliable, auditable, and safe, with an emphasis on evidence-grounded behavior, robustness to changing information, and careful evaluation in real-world settings. His work appears in venues including SIGIR, WSDM, EMNLP, IJCAI, and WebConf, and has also received broader media attention through MIT Technology Review. He brings experience from both academia and industry research, including roles at UW, Amazon Science, and AWS AI, and is an inventor on multiple U.S. patents.

Tanya Roosta

UC Berkeley School of Information & Amazon

Email: troosta@ischool.berkeley.edu

Tanya is a senior science manager at Amazon, working on generative AI techniques for natural language processing and information retrieval problems, and leading feature development for various aspects of Amazon Shopping. She concurrently holds a lecturer position at Department of Information Science at UC Berkeley. Prior to Amazon, she worked at an early-stage Fintech startup as the lead research scientist working on efficient topic modeling, sentiment analysis and social media trending-topic detection. Her research used deep neural networks, and advanced statistical modeling, and the resulting features were implemented through AWS APIs. Tanya also has over 9 years of work experience in quantitative finance and investment banking, working as a director of risk and finance analytics at Moody's, quantitative researcher at the Economic department of Federal Reserve Bank of San Francisco, and quantitative modeling for systematic portfolio management at Allianz. She holds a Ph.D. in Electrical Engineering, a Masters in Mathematical finance, and a Masters in Statistics. She has published in several conferences and journals, and holds patents as part of her industry work.

Citation

If you use this tutorial in your work or teaching, please cite:

@inproceedings{dammu2026information,
  title={Information Seeking in the Age of Agentic AI: A Half-Day Tutorial},
  author={Dammu, Preetam Prabhu Srikar and Roosta, Tanya},
  booktitle={Proceedings of the 2026 Conference on Human Information Interaction and Retrieval},
  pages={429--430},
  year={2026}
}

View on ACM DL  ·  Contact: Preetam Dammu <preetams@uw.edu>, PhD Candidate, University of Washington