Runtime continuity manifesto
Replay-native systems make behavior inspectable over time: what happened, what changed, what context traveled, and where continuity decayed. This page is ReconAI public posture and vocabulary, not a claim that every product surface is live for every workspace.
Illustrative handoff map only: continuity decay and divergence propagation are static concepts here, not live agent telemetry.
Replay-native architecture
Agents, tools, prompts, approvals, and model calls where work actually happens.
Ordered events, hashes, expected-vs-observed posture, and checkpoint markers.
Receipt vocabulary for preserving lineage, trust deltas, and review-ready evidence.
Static relationship map for handoffs, policy edges, drift, and recovery context.
Human review, escalation, retention, and recovery decisions grounded in replayable context.
Illustrative public architecture only. It describes ReconAI continuity vocabulary and review posture, not live customer telemetry, hidden orchestration, or a guarantee that every layer is enabled in every workspace.
Replay matters
AI systems make their most important choices across prompts, tool calls, retries, approvals, and handoffs. If those moments cannot be replayed later, operators are left reconstructing intent from fragments instead of reviewing the evidence chain.
Lineage
Lineage is the connective tissue between a decision, the context that shaped it, and the downstream state it influenced. Recon's public posture is that lineage should be preserved as reviewable operational evidence, not treated as incidental logging.
Runtime memory
Trust memory means durable references to decisions, checkpoints, posture shifts, and recovery paths. It is not consciousness, hidden cognition, or a claim that the system understands the past. It is persisted context that helps humans inspect what happened over time.
Continuity
A continuity-first runtime keeps enough structure around the work to compare before and after, identify divergence, and preserve review context when agents or tools hand work to one another.
Open replay standard
This is not an IETF, ISO, W3C, or government standard. It is Recon's public posture for talking about replayable AI runtimes, with schema echoes from LP-004-LP-010 where useful, and without claiming a universal wire protocol.
Named moments in a run where context, posture, or authority-sensitive state can be inspected again later.
Records that connect a decision or handoff to its predecessor, successor, and review context.
Small changes in trust posture, confidence, risk, or review state that explain why a run looks different from its prior state.
Lightweight context that helps replay systems preserve ordering, ownership, runtime surface, and reviewer-relevant references.
Replay API and SDK visibility
Trust memory narrative
Trust memory is operational memory: lineage preservation, replay persistence, and durable references to what a reviewer may need later. It should help teams compare a system to its prior state, not suggest that software has independent judgment or perfect recall.
Multi-agent workflows make this more important. A drift at one handoff can propagate into the next agent's context, so replay systems need continuity metadata that keeps divergence visible instead of burying it in downstream logs.