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<!doctype html>
<html lang="en">
<head>
<title>Agentic AI in University Administration — CIKM 2026 Shared Task</title>
<meta charset="utf-8">
<meta name="description" content="A shared task on agentic AI in university administration, part of the CIKM 2026 AnalytiCup: build agents that solve standardized administrative tasks under controlled tool access and expert-judged reference solutions.">
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<h1>Agentic AI in University Administration</h1>
<p class="uk-text-lead">A shared task at the <a href="https://cikm2026.diag.uniroma1.it/" target="_blank" rel="noopener">CIKM 2026</a> AnalytiCup, Rome, 8–11 November 2026.</p>
<ul class="uk-list">
<!-- Comment out sections you do not provide -->
<li><span data-uk-icon="chevron-down"></span> <a href="#synopsis">Synopsis</a></li>
<li><span data-uk-icon="chevron-down"></span> <a href="#task">Task</a></li>
<li><span data-uk-icon="chevron-down"></span> <a href="#data">Data & Tools</a></li>
<li><span data-uk-icon="chevron-down"></span> <a href="#evaluation">Evaluation</a></li>
<li><span data-uk-icon="chevron-down"></span> <a href="#participation">Participation</a></li>
<li><span data-uk-icon="chevron-down"></span> <a href="#dates">Important Dates</a></li>
<li><span data-uk-icon="chevron-down"></span> <a href="#awards">Awards</a></li>
<li><span data-uk-icon="chevron-down"></span> <a href="#results">Results</a></li>
<li><span data-uk-icon="chevron-down"></span> <a href="#related-work">Related Work</a></li>
<li><span data-uk-icon="chevron-down"></span> <a href="#task-committee">Task Committee</a></li>
<li><span data-uk-icon="chevron-down"></span> <a href="#contact">Contact</a></li>
</ul>
</div>
<div class="uk-container uk-margin-medium">
<!--
SECTION Synopsis
-->
<h2><a id="synopsis"></a>Synopsis</h2>
<p>Agentic AI systems combine large language models, information retrieval,
planning, and tool use to act on behalf of users in complex digital
environments. Yet their evaluation remains difficult for lack of reproducible,
bounded laboratories. A university administration is a closed-world,
socio-technical environment in which many tasks follow standardized,
well-documented processes — making it particularly suited for a
Cranfield-style evaluation of AI agents. The controlled, reproducible
evaluation of agentic AI in such enterprise-like settings is an underexplored
yet highly relevant problem.</p>
<p>This shared task offers the first reusable laboratory for the controlled
evaluation of agentic AI in a university environment, namely the University of
Kassel. Participants build agents that solve standardized administrative tasks
(e.g., travel permits and reimbursements) by retrieving the relevant
regulations, planning the necessary steps, and using a controlled set of tools,
while complying with the institution's codes of conduct. The document collection
is compiled together with the <a href="https://openwebsearch.eu" target="_blank" rel="noopener">OpenWebSearch.eu</a>
project, which also provides search and retrieval endpoints.</p>
<!--
SECTION Task
-->
<h2><a id="task"></a>Task</h2>
<p>In its most generic form, the class of tasks we study can be described as
follows:</p>
<blockquote class="uk-text-meta">
Given a digital environment and a problem or task (the objective) that is
typically tackled by a person acting in the environment, develop an intelligent
agent that achieves the objective on the person's behalf by planning and
executing the necessary steps in accordance with the environment's codes of
conduct.
</blockquote>
<h3 class="uk-margin-small-top">Topic areas</h3>
<p>The first edition focuses on three administrative task areas:</p>
<ul class="uk-list uk-list-bullet">
<li><b>Business travel</b> — travel permit requests, compliance
checking (e.g., hotel rate limits, private extensions), and reimbursement
processing. Topics are derived from labeled mail exchanges between
administrative staff and university staff.</li>
<li><b>Privately advanced costs</b> — reimbursement of
out-of-pocket expenses paid by university staff. The process is modeled as a
BPMN workflow and provided to participants.</li>
<li><b>Internal procurement</b> — identification, selection, and
ordering of goods through the university's procurement system, supported by
a dedicated product-search tool for reproducibility.</li>
</ul>
<h3 class="uk-margin-small-top">Sub-tasks</h3>
<p>To lower the barrier to entry, participants may address any of three
sub-tasks, each taking the task description as input:</p>
<ol class="uk-list uk-list-decimal">
<li><b>Retrieval</b> — retrieve documents relevant to the current
state and task progress (focus on recall); output the run and a log of
reasoning tokens.</li>
<li><b>Simulation</b> — generate a plan of action without
executing it; output the plan, retrieved documents, and reasoning tokens.</li>
<li><b>Solving</b> — build a working agent that synthesizes a full
solution; output the solution and a log of actions, retrieved documents, and
reasoning tokens.</li>
</ol>
<h3 class="uk-margin-small-top">Example topic</h3>
<figure class="uk-margin">
<img src="example-topic.png" style="max-width:380px;width:100%;border:1px solid #e5e5e5;border-radius:3px;"
alt="Example topic for travel reimbursement: an agent receives the travel regulations and BPMN reimbursement workflow, then processes a conference-trip reimbursement request with attached receipts (train, hotel, registration), producing the steps check, fill, and submit.">
<figcaption class="uk-text-meta uk-margin-small-top">Example topic for travel
reimbursement: a user query with attached documents, the expected agent output
following the prescribed process, and annotations for query type, complexity,
and rule area.</figcaption>
</figure>
<!--
SECTION Data & Tools
-->
<h2><a id="data"></a>Data & Tools</h2>
<p>The environment comprises a document collection, a set of topics, and a
controlled set of tools.</p>
<h3 class="uk-margin-small-top">Document collection</h3>
<p>Compiled by crawling the web pages of the University of Kassel and other
sources together with the <a href="https://openwebsearch.eu" target="_blank" rel="noopener">OpenWebSearch.eu</a>
project. A specialized sub-crawl is available for download, and the project
provides pre-computed embeddings and a RAG API endpoint for participants who
do not wish to index the collection themselves. First-edition tasks are selected
to have sufficient English documentation.</p>
<h3 class="uk-margin-small-top">Topics</h3>
<p>We compile a collection of 50 topics across the three task areas above,
drawing on the hands-on expertise of administrative staff trained in the
University of Kassel's “AI FrAIdays” seminar series. Each topic
consists of a user query (with any attached documents), an expected output
following the prescribed process, and annotations for query type, complexity,
and rule area.</p>
<h3 class="uk-margin-small-top">Tools</h3>
<p>Tool access is provided through one of the established AI agent protocols
(e.g., <a href="https://modelcontextprotocol.io/" target="_blank" rel="noopener">MCP</a>
or <a href="https://a2a-protocol.org/" target="_blank" rel="noopener">A2A</a>).
The following tools are envisioned for the first edition:</p>
<ul class="uk-list uk-list-bullet">
<li><b>All task areas:</b> RAG systems / chatbots indexing the relevant
document collections (hosted at OpenWebSearch.eu) and a subset of internal
documents; a mock mail backend for sending e-mails to fake addresses of the
administrative staff; and any locally available tools participants choose to
integrate via the prescribed agent protocol.</li>
<li><b>Business travel and privately advanced costs:</b> a BPMN endpoint
exposing the modeled university workflows, and a mock interface to the
university's SAP-based travel and reimbursement portal for filling in and
submitting forms without touching the live system.</li>
<li><b>Internal procurement:</b> a RAG endpoint over the university's
internal product catalog; where appropriate, public product catalogs may
additionally be consulted via web-search tools.</li>
</ul>
<p class="uk-text-meta">No personally identifiable information (names, mail
addresses, phone numbers) of University of Kassel staff is contained in the
collection or the mock backends.</p>
<h3 class="uk-margin-small-top">Input / Output format</h3>
<p><b>Input:</b> a task description (a user request, optionally with attached
documents). <b>Output</b> depends on the chosen sub-task and follows prescribed
formats: retrieved documents in the TREC run format, action logs in a common
protocol format, and reasoning tokens as plain text. Validation tools are
provided.</p>
<!--
SECTION Evaluation
-->
<h2><a id="evaluation"></a>Evaluation</h2>
<p>Because solutions to standardized tasks can be checked against a reference,
we combine manual and automatic evaluation. Each sub-task is scored on the same
gold topics, with criteria matched to its required output:</p>
<ul class="uk-list uk-list-bullet">
<li><b>(1) Retrieval.</b> Retrieved documents are judged against the set of
sources needed to make progress on the task, using recall-oriented IR
measures (Recall@k, MAP, nDCG). Recall is primary, since a missing regulation
or form invalidates the downstream solution.</li>
<li><b>(2) Simulation.</b> The proposed plan is compared against the reference
process (modeled, where available, as a BPMN workflow): we score the coverage
and precision of the plan's steps, the correctness of their ordering, and
whether the required decision points and documents are accounted for. The
supporting retrieved documents are scored as in the Retrieval sub-task.</li>
<li><b>(3) Solving.</b> The synthesized solution is scored on <i>task
success</i> — a binary criterion: the minimal solution accepted by
peers (e.g., a reimbursement application with valid receipts) — and,
among non-successful runs, on the number and severity of errors (rule
violations, missing receipts, incorrect form fields), which enable a finer
ranking. The agent behavior log additionally yields tool-use accuracy
(precision/recall of tool calls) and retrieval quality, and a qualitative
audit verifies the solution was reached for the right reasons rather than by
chance.</li>
</ul>
<p>Across all sub-tasks we also report <b>efficiency</b> measures: manual
task-completion time (a trained-human baseline), runtime, and environmental
impact, for which we investigate the TIREx tracker. Judges compare solutions
against expert reference solutions, ideally via a diff visualization; trained
laypeople suffice for most topics, with regulatory edge cases referred to domain
experts. Automatic LLM-as-judge measures will be validated against the human
reference-based judgments on a sample before being used to rank systems. The
organizers carry out the necessary judgments and assessments.</p>
<!--
SECTION Participation
-->
<h2><a id="participation"></a>Participation</h2>
<p>Participation is open to research groups and individuals from IR, NLP, AI,
and adjacent communities, as well as industry teams.</p>
<ol class="uk-list uk-list-decimal">
<li><b>Register to the shared task on TIRA.io</b> Please <a href="https://www.tira.io/signup">Sign Up in TIRA</a> and then please <a href="https://www.tira.io/task-overview/uniagent-2026/"> register to the UniAgent Task</a>.</li>
<li><b>Develop your agent</b> against the released document collection,
tools, and example topics. Tools are accessed via the prescribed AI agent
protocol; you may integrate your own local tools.</li>
<li><b>Submit your runs</b> in the prescribed formats together with the agent
behavior log. Validation tools are provided to check submission validity.</li>
<li><b>Results and findings</b> are presented at the CIKM 2026 AnalytiCup
session; participating teams are invited to contribute to the overview paper.</li>
</ol>
<!--
SECTION Important Dates
-->
<h2><a id="dates"></a>Important Dates</h2>
<p class="uk-text-meta">All dates are tentative and will be finalized upon
acceptance.</p>
<table class="uk-table uk-table-divider uk-table-small uk-table-hover">
<tbody>
<tr><td><b>July 2026</b></td><td>Launch; release of collection, tools, and example topics; registration opens.</td></tr>
<tr><td><b>August 2026</b></td><td>Team formation deadline; training topics released.</td></tr>
<tr><td><b>Mid-October 2026</b></td><td>Submission deadline for system runs.</td></tr>
<tr><td><b>Late October 2026</b></td><td>Evaluation period (expert judgments and automatic measures).</td></tr>
<tr><td><b>Early November 2026</b></td><td>Notification of results.</td></tr>
<tr><td><b>8 November 2026</b></td><td>Presentation at the CIKM 2026 AnalytiCup (main conference 9–11 November).</td></tr>
</tbody>
</table>
<!--
SECTION Awards
-->
<h2><a id="awards"></a>Awards</h2>
<p>Winning teams are recognized with certificates, a presentation slot at the
CIKM 2026 AnalytiCup session, and a mention in the overview paper. We
foresee no monetary prizes at this stage, though awards may be extended if
sponsorship becomes available.</p>
<!--
SECTION Results
-->
<h2><a id="results"></a>Results</h2>
<p>Results will be announced after the evaluation period — see
<a href="#dates">Important Dates</a>.</p>
<!--
SECTION Related Work
-->
<h2><a id="related-work"></a>Related Work</h2>
<p>This task builds on and complements prior community evaluations:</p>
<ul class="uk-list uk-list-bullet">
<li><b>TREC Enterprise tracks (2005–2008).</b> We revisit the
enterprise setting with a more holistic aim: rather than retrieving
information for a query, we automate achieving the objective, of which
retrieval is a sub-task.</li>
<li><b>TREC Million LLM track (2025–2026).</b> Retrieving expert LLMs
for specific tasks complements ours, as an agent may consult such an expert
while executing its plan.</li>
<li><b>TREC RAG track (2024–2026).</b> A RAG response is a synthesized
solution integrating retrieval, but does not include tool calling or richer
environment interactions.</li>
<li><b>TREC AutoJudge track (2026)</b> and the <b>CLEF</b> evaluation labs
(e.g., Touché, LongEval), with which integration and shared expertise
are foreseen.</li>
</ul>
<!--
SECTION Task Committee
-->
<h2><a id="task-committee"></a>Task Committee</h2>
<!-- TODO: replace silhouette placeholders with photos and add affiliation logos / profile links. -->
<div data-uk-grid class="uk-grid uk-grid-match uk-grid-small thumbnail-card-grid">
<div>
<div class="uk-card uk-card-default uk-card-body">
<a href="https://orcid.org/0000-0003-2451-0665" class="uk-link-reset">
<div class="thumbnail-left"><img src="https://webis.de/img/people/potthast.jpg" alt="Martin Potthast"></div>
<div class="uk-height-1-1">
<div class="uk-card-title">Martin Potthast</div>
<div class="uk-text-muted uk-margin-small-top uk-margin-small-bottom">University of Kassel, hessian.AI, ScaDS.AI · Track coordinator (evaluation design, reproducibility)</div>
</div>
</a>
</div>
</div>
<div>
<div class="uk-card uk-card-default uk-card-body">
<a href="https://orcid.org/0000-0002-5503-0341" class="uk-link-reset">
<div class="thumbnail-left"><img src="https://webis.de/weimar/people/img/silhouette-male.jpg" alt="Udo Kruschwitz"></div>
<div class="uk-height-1-1">
<div class="uk-card-title">Udo Kruschwitz</div>
<div class="uk-text-muted uk-margin-small-top uk-margin-small-bottom">University of Regensburg · Enterprise search and assessment design</div>
</div>
</a>
</div>
</div>
<div>
<div class="uk-card uk-card-default uk-card-body">
<a href="https://orcid.org/0000-0003-3566-5507" class="uk-link-reset">
<div class="thumbnail-left"><img src="https://webis.de/weimar/people/img/silhouette-male.jpg" alt="Michael Granitzer"></div>
<div class="uk-height-1-1">
<div class="uk-card-title">Michael Granitzer</div>
<div class="uk-text-muted uk-margin-small-top uk-margin-small-bottom">University of Passau · Document collection, crawling, RAG infrastructure (OpenWebSearch.eu)</div>
</div>
</a>
</div>
</div>
<div>
<div class="uk-card uk-card-default uk-card-body">
<a href="https://orcid.org/0009-0003-1747-5643" class="uk-link-reset">
<div class="thumbnail-left"><img src="https://webis.de/weimar/people/img/silhouette-male.jpg" alt="Michael Dinzinger"></div>
<div class="uk-height-1-1">
<div class="uk-card-title">Michael Dinzinger</div>
<div class="uk-text-muted uk-margin-small-top uk-margin-small-bottom">University of Passau · Document collection, crawling, RAG infrastructure (OpenWebSearch.eu)</div>
</div>
</a>
</div>
</div>
<div>
<div class="uk-card uk-card-default uk-card-body">
<a href="https://orcid.org/0000-0002-2888-4202" class="uk-link-reset">
<div class="thumbnail-left"><img src="https://webis.de/weimar/people/img/silhouette-male.jpg" alt="Arjen P. de Vries"></div>
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<h2><a id="contact"></a>Contact</h2>
<p>For questions about the task and to register your interest, contact the
organizers at <a href="mailto:simon.ruth@uni-kassel.de">simon.ruth@uni-kassel.de</a>
and <a href="mailto:martin.potthast@uni-kassel.de">martin.potthast@uni-kassel.de</a>.</p>
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