MIZ OKI 3.5 — Verifiable Autonomous Decision Intelligence

Cognitive Architecture

Seven-stage autonomous intelligence loop

Every decision flows through a verifiable pipeline. PLAN and VALIDATE stages ensure proposals are challenged before the Decision Control Plane authorizes execution.

S

Sense

R

Reason

P

Plan

V

Validate

D

Decide

A

Act

L

Learn

↩ LEARN feeds outcomes back into SENSE — continuous improvement from real results

Core Innovations

What makes 3.5 different

Four architectural breakthroughs that enable verifiable, governed autonomy for enterprise and regulated environments.

DCP

Decision Control Plane

Centralized authority that receives proposals from autonomous agents and authorizes, rejects, modifies, or defers them. Agents cannot bypass the DCP — this separation of proposal from authorization is a fundamental architectural constraint.

VAL

Validation & Arbitration Layer

Planner, Verifier, Risk, and Policy agents independently evaluate each proposal. Disagreement is signal, not error. Arbitration resolves conflicts based on confidence thresholds and historical accuracy.

CSE

Counterfactual Simulation Engine

Before execution, simulate the proposed action, alternatives, and a no-action baseline. Learn from simulated outcomes without real-world risk. Authorization denied when alternatives score higher.

TCO

Temporal-Causal Knowledge Graph

Decision memory and audit spine. Stores facts, proposals, simulations, outcomes, and causal confidence with timestamps. Every decision is traceable. Every outcome feeds learning.

Measurable Impact

Operational intelligence, not analytics

Every action is logged. Every decision is auditable. Performance improves continuously through the Learn cycle.

50–75×

Faster decision velocity

↓35%

Revenue leakage reduction

↓41%

Operational cost reduction

3.2mo

Typical payback period

89%

Automation coverage

The Pipeline

Autonomous Decision Controllers

Each stage is powered by a specialized ADC with explicit algorithms, audit trails, and adaptive feedback mechanisms.

S

Sense SENSE-ADC

Autonomous attention allocation calculates priority scores based on impact magnitude, uncertainty, time criticality, and strategic alignment. Ingests live signals from ERP, CRM, market data, and operations. Dynamic thresholds adapt to system load.

R

Reason REASON-ADC

Causal GraphRAG traverses the knowledge graph using causally-informed queries — not just semantic similarity. Automated confounder detection. Analysis depth scales with decision value and uncertainty reduction potential.

P

Plan PLAN-ADC

Generates candidate actions through causal reasoning over the knowledge graph. Multi-objective optimization considers probability, value, and ethical alignment. Multiple alternatives are always generated.

V

Validate VALIDATION LAYER

Independent Verifier, Risk, and Policy agents challenge every proposal. Disagreement is measured and weighted. The system doesn’t require consensus — arbitration resolves based on confidence thresholds.

D

Decide DECISION CONTROL PLANE

The DCP calculates authorization scores combining causal confidence, validation agreement, simulation delta, policy compliance, and historical performance. Approve, modify, defer, reject, or escalate.

A

Act ACT-ADC

Orchestrates execution with dependency resolution and adaptive rollback. Performance deviation triggers automatic rollback strategies — immediate, gradual, partial, or checkpoint-based.

L

Learn LEARN-ADC

Prioritizes learning by prediction error, business impact, knowledge gap, and frequency. Updates models with stability-plasticity balance. Feeds outcomes back into SENSE for continuous improvement.

Featured Use Case

Media Buying Intelligence with Threshold Awareness

Meta’s ad system behaves non-linearly. Below thresholds, effort feels invisible. Above thresholds, learning compounds. MIZ OKI 3.5 understands these dynamics through its Counterfactual Simulation Engine.

The platform models Meta’s delivery and optimization as a ReLU activation function — below certain thresholds, nothing really happens; above them, momentum kicks in. The CSE finds optimal operating points across these non-linear dynamics.

Learning Phase Detection

Detects when ad sets trend toward “Learning Limited” (~50 events/week) and recommends consolidation before performance degrades.

Signal Quality Optimization

Monitors Event Match Quality, deduplication accuracy, and funnel completeness. Clean tracking as voltage — you can’t cross thresholds with noisy instrumentation.

Institutional Memory

Knowledge Graph captures what you tested, what changed, what worked, and under what conditions. Stop reinventing the wheel every campaign.

Stability Windows

Protects learning by limiting edits that reset optimization dynamics. Campaign management as a system, not a reaction.

Industry Templates

Pre-configured for your domain

Deploy in weeks, not months. Each template includes pre-trained causal models, agent configurations, and compliance frameworks.

Retail & Commerce

Demand forecasting · Pricing optimization · Inventory risk · Customer behavior models

Finance & Capital

Risk exposure · Scenario simulation · Portfolio automation · Fraud patterns

Operations & Supply Chain

Bottleneck prediction · Cost optimization · Disruption response · Production flows

Healthcare

Compliance automation · Resource optimization · Clinical pathways · Outcome forecasting

Media Buying

Attribution models · Channel optimization · Creative performance · Threshold-aware bidding

Manufacturing

Quality models · Maintenance schedules · Supply chain optimization · Production optimization

Enterprise Ready

Built for boards, CISOs, and regulators

Full decision traceability. Explainable reasoning. Quantum-resistant security. Deploy on your cloud or ours.

Quantum-Resistant Security

CRYSTALS-Kyber key encapsulation, CRYSTALS-Dilithium signatures. Future-proof cryptography ready for post-quantum threats.

Immutable Audit Logs

Every proposal, validation, simulation, and execution is cryptographically signed and logged with full chain of custody.

Human-in-the-Loop Controls

Full override capability. Configurable thresholds. Automatic escalation for novel situations or low-confidence decisions.

Multi-Tenant Isolation

Kubernetes namespace isolation. Tenant-specific knowledge graphs. Federated learning with differential privacy guarantees.

Explainable Decisions

Causal pathways for every decision. Transparent reasoning chains. No black-box mystery. Regulator-ready documentation.

Sub-100ms Latency

Operational queries under 100ms. Full decision cycle under 60 seconds. Real-time intelligence at enterprise scale.

Decisions verified before execution — not explained after failure

Media Buying Intelligence with Threshold Awareness