Architecture brief
Forensic Replay · Governed Intelligence Architecture
Forensic replay is the proof layer of governed intelligence. It preserves the evidence required to reconstruct what happened, who or what initiated it, what context influenced it, which policies applied, which model was used, what actions were authorized, and what changed afterward.
Replay thesis
Logs show that something happened. Forensic replay shows how it happened, why it happened, what governed it, and whether the system behaved within authorized boundaries.
Why replay matters
Operational Accountability
Teams need to prove whether an AI-mediated action followed authorized policy and approved scope.
Regulatory Readiness
Regulated environments require evidence, not narrative, when automated systems influence consequential outcomes.
Incident Response
Security and compliance teams need to reconstruct the decision chain after failures, anomalies, abuse, or disputes.
Model Independence
Evidence must remain available even when providers change, models are upgraded, prompts evolve, or tools are replaced.
The architecture
Replay input
Forensic replay begins with identity: the user, role, organization, session, device context, authorization state, and delegated authority involved when the interaction started.
Replay governance
The replay chain must preserve policy version, risk classification, permission result, boundary evaluation, escalation state, and approval or denial outcome.
Context trace
Replay requires a record of retrieved memories, documents, system context, user state, institutional policy, and any external data consulted.
Model trace
The chain preserves provider, model, configuration, route, prompt assembly, context budget, safety layers, latency, and cost signals.
Execution trace
Replay records tool calls, API requests, database changes, notifications, workflow steps, approval gates, and post-action state changes.
Replay packet
A replay packet is the structured record that lets a human, auditor, regulator, security team, or executive understand how an AI-mediated decision was produced.
1. Actor
User, role, organization, device, session, and authority state.
2. Intent
Original request, detected intent, risk category, and operational classification.
3. Context
Retrieved memories, documents, policies, user state, and relevant system context.
4. Governance
Policy version, rule outcomes, risk score, permission result, escalation state, and decision rationale.
5. Model
Provider, model, route, configuration, prompt assembly, token use, latency, and response metadata.
6. Action
Tool calls, API activity, database writes, workflow steps, output delivery, and post-action state.
Replay capabilities
Decision Reconstruction
Rebuild the chain from user intent through model inference and operational outcome.
Policy Verification
Confirm whether the correct policy version was applied and whether the result complied with it.
Model Route Review
Show which provider, model, and configuration produced the inference.
Tool and Execution Review
Trace whether an AI action reached a tool, API, workflow, database, or external service.
Memory Impact Review
Determine what was stored, updated, summarized, discarded, or made retrievable later.
Executive Evidence View
Give leaders a clear, non-technical explanation of what happened and why it was allowed.
Rita + Palladium + Origin
Rita defines the governance logic that replay must preserve: authority chains, trust boundaries, policy results, escalation rules, and decision rationale.
Understand Rita
Palladium operationalizes replay for organizations: event capture, trace correlation, audit packets, incident review, evidence preservation, and reconstructable timelines.
Explore Palladium
Origin makes personal intelligence reconstructable without making it captive: identity, memory, consent, context, and persistence events can be reviewed while remaining user-owned and provider-independent.
Explore OriginReplay timeline
1. Request Received
Intent, actor, role, session, and initial classification are captured.
2. Context Assembled
Relevant memory, documents, policies, and state are retrieved under boundary controls.
3. Governance Applied
Policy, permissions, risk, age, role, and escalation rules determine authorization.
4. Model Routed
The approved provider, model, prompt, and tool path are selected and recorded.
5. Action Performed
Output, tool calls, database activity, or workflow execution are captured.
6. Evidence Preserved
Replay packet, persistence choices, memory impact, and audit chain are retained.
Architecture pages
Ananke Inc.
Ananke makes AI decisions reconstructable across identity, context, policy, model routing, execution, persistence, and audit.
