Reconstructing an Embargo Breach in a Multi-Agent Corporate Communications System Using Visual Analytics

Authors

Lauma Ernestsone (Utrecht University), Angelos Chatzimparmpas (Utrecht University)

Abstract

We present a visual analytics (VA) system for reconstructing the embargo breach in the VAST Challenge 2026 MiniChallenge 1. The dataset contains 912 timestamped messages exchanged across 23 rounds by seven AI agents managing TenantThread’s communications during a confidential merger. The interface combines a pixel-based corpus overview, a round-level inspector of messages and internal reasoning, and an authored Story Arc linking a curated 92-message subset through typed causal relationships. Coordinated interactions support analysis from corpus-level patterns to individual evidence and the reconstructed causal sequence. Our analysis shows that the breach was not a sudden crisis-day failure: compliance monitoring began late, an earlier near-breach left the monitor unable to detect the eventual failure mode, and the legal justification for disclosure preceded the external event later invoked to justify it. The legal agent prepared the release and ordered publication before seeking CEO authorization, demonstrating how VA can reveal both deliberate action and the structural weaknesses that enabled it.