August 09, 2026, Jeju, Korea. Held in conjunction with KDD'26, International Convention Center Jeju (ICC Jeju)
This workshop advances evaluation and trustworthiness methodologies for agentic AI systems across their full deployment lifecycle, with particular emphasis on real-time post-market monitoring, model evolution, and production governance. As autonomous agents increasingly perform multi-step reasoning, planning, and action in open-ended real-world settings, traditional pre-deployment benchmarks and static evaluation frameworks prove insufficient.
We address core challenges including stochastic agent behavior, absence of ground truth, evolving user contexts, API-driven model updates, and lack of standardized metrics and audit practices. This workshop aims to foster interdisciplinary collaboration by bringing together researchers, industry practitioners, and policymakers to develop advanced evaluation techniques and governance frameworks for agentic AI systems that can be safely and reliably deployed in production.
Contact: kdd-ws-agentic-eval@amazon.comThis workshop focuses on the unique challenges of evaluating and ensuring trustworthiness of agentic AI systems throughout their deployment lifecycle. As large language models and autonomous agents are increasingly deployed in real-world, open-ended settings, we need new methods and frameworks that go beyond traditional pre-deployment benchmarks. Topics of interest include (but are not limited to):
Arthur S. Pearse Distinguished Professor of Computer Science at Duke University
Title: Beyond the Single Turn: Decomposing Evaluation for Multi-Party, Long-Horizon Agentic AI
Abstract: Imagine an AI agent that joins a year‑long software development project—navigating heated debates between engineers, remembering who approved which design revision, and drafting a reply that sounds like the quiet backend expert, not the outspoken product lead. Today, no evaluation framework can tell us whether such an agent actually works, because our metrics were built for simpler worlds: two‑person chats, single‑reference answers, and static facts retrieved from clean paragraphs. This talk will tear open the black box of multi‑party conversation with MPCEval, a benchmark that asks three separate questions—who should speak next, what should they say, and does the content fit the speaker?—revealing that human conversations are not a gold standard, that models shine in surprisingly different ways, and that collapsing everything into one number is a recipe for deception. Then we will step into EverMemBench, a year‑long simulated enterprise where memory systems must piece together fragmented evidence across groups, track decisions that get revised and superseded, and infer unspoken traits like communication style. The results are humbling: even the best models stumble over cross‑group attribution, fail to understand "completed" versus "archived" as semantic states, and cannot retrieve a person's casual, emoji‑laced voice even when they have all the facts. Together, these findings point to a new path forward—where evaluation stops pretending that longer contexts or bigger retrieval lists are enough, and instead embraces the messy, distributed, time‑aware, and socially grounded reality of how humans actually collaborate. This is not just about better benchmarks; it is about reimagining what we mean by "intelligence" in agentic AI.
Leonard C. Bettex Collegiate Professor of Computer Science, Department of Computer Science and Engineering, University of Notre Dame
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