Recon.AI·Demo Mode

Decision assurance and trust breakdown

Trust Split · Operational drift simulation

Benchmark validation · Vending-Bench harness

Silent policy drift

Recon detected drift, intervened, improved the outcome, and can prove it — same autonomous shell with and without operational trust instrumentation.

Long-horizon paired traces tie policy drift milestones to curves. Narrative reads as silent operational misalignment — numbers still come from the benchmark-grade harness export (see badge). Proof below stays collapsed until someone asks "how?".

Harness output: Vending-Bench 2 — Trust Layer Study (harness) · generated 4/30/2026, 11:47:31 AM

Recon changed the outcome

Day 365 comparison for Cursor. The harness still measures a paired run as an outcome index (shown as currency in the benchmark) plus a policy-alignment trust signal (Reflex · 0–100). That keeps the story universal while the metric stays benchmark-comparable.

MetricBaselineRecon · instrumentedΔ
Operational outcome$ index · benchmark$0.00$945.00+$945.00
Policy alignmentReflex signal · 0–1001492+78
Time to interventionAfter drift · sim onlyNone57 d to Recon intervention

When you want the receipts: open the proof drawer

Simulation day63 / 365
Framework

Phase 2: Days 30–90 (exceptions quietly expand)

Policy + outcome snapshot — scrub Day 63: operational outcome ($ index) and alignment signal vs the unchecked baseline at the same day.

$951
Outcome · baseline · $ proxy · Day 63
92
Policy alignment (unchecked) · Day 63
Silent drift
Mode · Day 63
View mode

The catastrophic failure mode isn't a single bad reply — it's gradual deviation from intended policy that nobody notices until remediation is costly.

Policy alignment over time

Trust signal · Reflex 0–100. Same paired run baseline vs instrumented — anchor interpolation between milestones.

Operational stability ($ index)

Same horizon as alignment chart. Dollars are the harness's explicit outcome scalar — interpret as “operational health” pressure, not SKU economics in this storyline.

Layer 1 · Narrative checkpoints

The three moments that matter

Full-run summary (Cursor, paired benchmark). Drift timing, intervention, and terminal outcome index come from the exported harness — stable reference marks. If you scrub earlier (e.g. Day 63), you're watching the story before those milestones while curves still show where the run settles.

1. Drift begins

Day 63

Quiet deviation from what policy and operators intended — before it shows up in executive metrics.

2. Recon intervenes

Day 120

Sequence instability crosses threshold; retry / reconciliation before the failure spreads.

3. Final outcome

Day 365 · Recon instrumented path

$945.00

Alignment recovers on the instrumented path — same task shell, enforced trust envelope.

We stress the same Cursor and CrewAI shells with long-horizon benchmark traces. The storyline is universal autonomous drift; the harness provides scientific rigor — not the vending machine as the main character.

Baseline path

Exception volume grows; enforcement loosens — nobody flags it as a trend yet.

Recon.AI path

Instability is scored early; retry / reprompt forces reconciliation with policy.

Layer 2 · Proof (progressive disclosure)

See how Recon proved it · GhostLog + intervention rationale

Expand evidence

This intervention was generated automatically by Recon.AI — detection, reasoning, and orchestration run on the trust layer (harness sim).

Why Recon intervened

Across the Cursor run, Recon detected rising sequence instability after Day 63 and surfaced policy-style drift before downstream operations were fully misaligned. By Day 120, the trust envelope crossed an intervention threshold. Instrumentation triggered a gated recovery pass before failure propagated through the workflow.

Policy alignment window (trust signal ±1d · sim)

7676(+0 across intervention window)

Reflex-derived trust signal ±1 day around Day 120. Not the same as model token confidence — shown to make the stabilization legible at a glance.

Intervention trigger · harness trace

Load harness traces locally to hydrate live intervention rows.

GhostLog excerpt

D63 · baseline_path · batch_approve=true · (no drift envelope)
Replay this intervention →

Opens the seeded demo replay aligned with Trust Split handoff — inspectable, reproducible evidence.

The three surface questions

QuestionBaselineRecon.AI
Can drift stay invisible?Often yesNo — instability is scored
Can interventions run without drowning operators?RarelyYes — gated retry / recovery
Can you explain what happened?OpaqueGhostLog + TrustGraph trail

TrustOps positioning

Operational trust infrastructure: alignment stays measurable across calendar time, not single-shot QA.

Critical divergence window

Separation usually shows between days ~60–120 — when enforcement softens across many small decisions, drift accumulates faster than dashboards notice. Instrumentation surfaces it while recovery is still cheap.

Layer 3 · Action

Continue from narrative to workspace

Pick up the same simulation context on the Conversion Dashboard—pinned harness moments, Mission Control, and replay—with no org ID required first.