Behavior Intelligence, Causality & Replay

A trace shows the technical path. Behavior Intelligence shows what shaped the outcome.

Inspect the context, authority, policy, decisions, models, tools, human approvals, attempts, and external effects that became one business outcome.

Outcome-first investigation start at the business effect
Evidence Correlation Spine connect every application identity
Reconstruct history without repeating the effect.
Application evidence

Connect runtime evidence to application meaning

Execution context

Which Execution Instance, application version, proposal, context, and authority shaped the outcome?

Decision path

Which policy decision, routing record, model result, validation, and human approval participated?

Business effect

Which Business Operation Identity, attempts, reconciliation, and Effect Receipt explain the final state?

Correlation spine

Keep one connected evidence spine across the application

Trace ID, proposal ID, policy decision ID, approval ID, Attempt ID, Business Operation Identity, and receipt ID solve different problems. Frozion preserves the relationships among them.

Published GraphExecution InstanceContextProposalDecisionApprovalEffect
Replay fidelity

Declare how faithful reconstruction can be

Historical replay can identify what is preserved, resolved from versioned artifacts, reconstructed, substituted, simulated, or unavailable.

Replay boundary

Keep replay isolated from production effects

Side-effecting nodes can be denied, stubbed, substituted, sandboxed, or simulated so investigation does not change business state.

Defensible explanation

Keep application causality precise

Behavior Intelligence should not overclaim access to private model reasoning. It explains the application-visible decision boundary.

What information was eligible and selected
Which model or capability version ran
Which policy, validation, and human judgment applied
Which action eventually committed
Inspection to improvement

Inspect one execution or compare many

Single execution

Why did this supplier change require human review, which policy applied, and what effect was confirmed?

Cross-execution analysis

Which route causes escalation, which obligation causes waits, and which capability produces abstentions?

Scoped projections

Analyze by tenant, application version, graph node, policy, capability, model, route, reviewer, and outcome.

Replay mode

Choose replay mode explicitly

1

Inspect-only

Read preserved evidence and realized execution path.

2

Historical reconstruction

Rebuild historical graph, state, context, decisions, waits, attempts, and effects.

3

Counterfactual simulation

Run a historical case against alternate policy, model, routing, threshold, or capability.

4

Controlled re-execution

Permit a current action only after present-day safety gates.

Start from one business outcome and reconstruct what shaped it.

Use an architecture review to identify the application identities, evidence, replay modes, and safety boundaries needed for one consequential AI process.