Visual Analytics for Multi-Agent Event Logs

Authors

Rients Dotinga (Utrecht University), Angelos Chatzimparmpas (Utrecht University)

Abstract

We present a visual analytics (VA) system for investigating an anomalous SaidIt post in the VAST Challenge 2026 Mini-Challenge 2. The dataset contains 185,147 timestamped events involving people, autonomous agents, files, tasks, communication systems, and external services. Because case identifiers are absent, related events are reconstructed using deterministic rules based on actors, tasks, filenames, message metadata, and content sources, producing 50,471 event traces. The interface combines aggregated event-sequence analysis, detailed case inspection, supporting interaction and temporal views, and process mining. Normal SaidIt cases are used to discover a baseline process model, against which suspicious traces are evaluated using conformance checking. Our analysis traces the post from John Windward’s account to SwiftWren.txt, which propagated through agent task handoffs before publication and file deletion. HiddenOrca and MellowOtter followed the same pattern and strongly deviated from normal SaidIt behavior, demonstrating how VA and process mining can reveal recurring anomalies in complex agentic systems.