Replay-native outreach kit
Public language for explaining replay-native AI systems.
A static in-repo kit for talks, essays, social posts, and diagrams about runtime continuity, replay-safe evidence, and GhostLog-compatible operating posture.
Educational discussion material only. This page is not financial advice, legal advice, a compliance opinion, or a claim that any external standard has ratified these terms.
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.
Diagram assets
Use these public pages as source material for diagrams, architecture references, and schema discussion.