KDD Workshop on Evaluation and
Trustworthiness of Agentic AI

KDD 2026

August 09, 2026, Jeju, Korea. Held in conjunction with KDD'26, International Convention Center Jeju (ICC Jeju)


Welcome to KDD Workshop on Evaluation and Trustworthiness of Agentic AI 2026!

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.com

Call for Contributions

This 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):

  • Real-Time Post-Market Monitoring: Continuous evaluation of deployed agentic systems, including drift detection, anomaly identification, performance degradation tracking, and monitoring under evolving user populations and contexts.
  • Agentic AI Evaluation: Assessing autonomy, multi-step reasoning, planning and tool use, goal alignment, adaptability, emergent failure modes, and multi-agent orchestration in dynamic environments.
  • Model Evolution and API Risk: Evaluation methods for detecting regressions, capability shifts, and safety risks introduced by model updates, version changes, and upstream dependency modifications.
  • Trustworthiness and Safety: Evaluation of reliability, bias and fairness, privacy, misuse resistance, robustness to distribution shift, explainability of agent actions, and safety guarantees.
  • Benchmarking, Metrics, and Standardization: Agent-centric benchmarks, LLM-as-judge methods, standardized metrics and logging protocols, evaluation frameworks for compound AI systems, and best practices for production monitoring.
  • Lifecycle and Governance Frameworks: End-to-end evaluation spanning pre-training, fine-tuning, deployment, and post-market phases, including auditability, liability attribution, regulatory compliance, and alignment with emerging AI governance standards.
  • User-Centric and Cross-Modal Assessment: Human-centered evaluation, trust calibration, human-in-the-loop systems, and assessment of agent behavior across text, image, audio, video, and multimodal inputs.
  • Industrial and Public-Sector Applications: Case studies of real-world deployments, enterprise-scale monitoring systems, sector-specific requirements (healthcare, finance, customer service), and scalable evaluation infrastructure for agentic AI.

Keynote Speakers

Jian Pei

Jian Pei

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.

Xiangliang (Lynn) Zhang

Xiangliang (Lynn) Zhang

Leonard C. Bettex Collegiate Professor of Computer Science, Department of Computer Science and Engineering, University of Notre Dame

Title: TBD

Abstract: TBD


SCHEDULE

TBD


Accepted Papers

TBD

Submission Guidelines

  • Please ensure your paper submission is anonymous.
  • The accepted papers will be posted on the workshop website but will not be included in the KDD proceedings.
  • Paper submissions are limited to 9 pages, excluding references, must be in PDF and use ACM Conference Proceeding templates.
  • Additional supplemental material focused on reproducibility can be provided. Proofs, pseudo-code, and code may also be included in the supplement, which has no explicit page limit. The supplement format could be either single column or double column. The paper should be self-contained, since reviewers are not required to read the supplement.
  • The Word template guideline can be found here: link
  • The Latex/overleaf template guideline can be found here: link
  • A paper should be submitted in PDF format through OpenReview at the following link: OpenReview Submission Portal

Camera-Ready & Poster Submission Guidelines

Special Day/Workshop Posters

  • Each board is vertical oriented; 965mm x 1,698mm (3.16 ft x 5.5 ft).
  • Each face of the board will fit (1) poster; (2) posters total per board.
  • Each paper will have 3 ft wide by 3.5 ft tall space for their poster.
  • Authors can decide on the size and design their poster, as they see fit (landscape or vertical), as long as it fits within that space.
  • KDD will provide push pins for each board.
  • The boards will be labeled for each Special Day/Workshop for attendees to easily locate.

Special Day/Workshop Setup/Teardown

  • All poster sessions will be held in ICC 1 - 3F between Halla and Samda Hall.
  • After the workshop/special day ends, presenters should remove their posters. Any posters left in the ICC may be discarded.
Poster Setup Layout

Organizers



Sadid Hasan

Sadid Hasan
Microsoft

George Karypis

George Karypis
Amazon & UMN