Public language for explaining replay-native AI systems.

Suggested talk titles

• •Replay-native AI: why agent systems need reconstructable memory
• •From observability to continuity: operating AI after the session ends
• •GhostLog-style evidence and the next interface for accountable autonomy
• •Replay as runtime posture: how teams inspect drift without exposing every payload

Essay theses

• •Agent trust should be grounded in continuity evidence, not one-off screenshots or chat transcripts.
• •Replay-native systems preserve enough ordered context to reconstruct what changed, who approved it, and where recovery began.
• •Governance improves when teams can compare expected runtime posture with observed behavior across tools and handoffs.
• •Honest replay language should separate educational doctrine, product posture, and any future certification claims.

Social thread outline

• •Open with the problem: AI workflows now cross tools, approvals, and agents faster than human review can follow.
• •Define replay-native: ordered runtime evidence, lineage references, checkpoints, and recovery context.
• •Show why this is different from logs alone: replay is about reconstructing continuity, not storing every private payload.
• •Close with the call to discuss open schema shapes, shared vocabulary, and replay-safe operating practices.