Four-Layer Cognitive Stack

Each layer serves a distinct purpose in the autonomous intelligence pipeline, from data foundation to secure integration.

L1
Foundation

Data Layer — Enhanced Self-Healing Knowledge Graph

A hybrid knowledge graph architecture that serves as the central dynamic knowledge core. Integrates multiple databases optimized for different workloads while maintaining consistency through versioned metadata and automatic reconciliation.

TigerGraph (Analytical) Neo4j AuraDB (Operational) Vector Search Decision Logic Engine
L2
Intelligence

Decision-Making & Learning Layer

Combines Causal GraphRAG operations, temporal causal inference, mixture-of-experts routing, and reinforcement learning. Derives context, proposes actions, and adapts policies over time based on outcome feedback.

Causal GraphRAG Temporal Causal Models Dual-level MoE RL Policy Optimization
L3
Execution

Autonomous Execution Layer

Houses the Autonomous Decision Controllers (ADCs) and coordinates the SRDPV-DAL pipeline. Implements agentic workflows, dynamic expert creation, continuous validation, and performance optimization while enforcing policies.

ADC Pipeline Decision Control Plane Validation Layer Counterfactual Simulation
L4
Integration

Integration & Privacy Layer

Provides secure APIs, federated learning, and multi-tenant isolation. Uses differential privacy techniques and quantum-resistant encryption to protect sensitive data while enabling cross-tenant model improvement.

RESTful APIs Federated Learning Differential Privacy Quantum-Resistant Crypto

Four Architectural Breakthroughs

What transforms MIZ OKI 3.5 from an AI system into a verifiable, governed autonomous decision platform.

Decision Control Plane

DCP

The centralized logical authority that receives all proposed actions from autonomous agents. Evaluates proposals against policy, confidence thresholds, and simulation results before authorizing execution.

  • Multi-factor authorization algorithm
  • Dynamic threshold adaptation
  • Approve / Modify / Defer / Reject / Escalate
  • Agents cannot bypass the DCP
  • Complete audit log of all decisions
🔍

Validation & Arbitration Layer

VAL

Multiple specialized agent roles independently evaluate each proposed action. Planner, Verifier, Risk, and Policy agents use separate analysis pathways to challenge proposals.

  • Independent evaluation pathways
  • Disagreement as signal, not error
  • Weighted arbitration based on accuracy
  • No consensus required for authorization
  • Confidence-based resolution
🔮

Counterfactual Simulation Engine

CSE

Before execution, simulates the proposed action, one or more alternatives, and a no-action baseline. Uses causal models from the knowledge graph and Monte Carlo methods for uncertainty quantification.

  • Proposed action + alternatives + baseline
  • Monte Carlo uncertainty quantification
  • Causal models from knowledge graph
  • Learn from simulated outcomes
  • Deny authorization when alternatives score higher
🕸️

Temporal-Causal Knowledge Graph

TCO-KG

Decision memory and audit spine. Stores not just facts and relationships, but also proposals, validation results, simulations, executed actions, and observed outcomes with timestamps and provenance.

  • Facts, proposals, simulations, outcomes
  • Timestamps, decay functions, confidence
  • Originating agent identifiers
  • Temporal validity windows
  • Bayesian confidence updates

Built on Proven Enterprise Infrastructure

Production-grade components selected for scale, reliability, and security.

Knowledge Graph Infrastructure

  • TigerGraph — Analytical queries, 10M+ token context
  • Neo4j AuraDB — Operational queries, <100ms latency
  • Vertex AI Vector Search — Semantic operations
  • Custom Decision Logic Engine

ML & Causal Inference

  • Causal GraphRAG with autonomous depth selection
  • PC Algorithm, GES, LiNGAM discovery
  • Dual-level Mixture of Experts
  • Reinforcement learning for policy optimization

Security & Cryptography

  • CRYSTALS-Kyber — Key encapsulation (NIST PQC)
  • CRYSTALS-Dilithium — Digital signatures (NIST PQC)
  • Autonomous key rotation
  • Hardware security module integration

Deployment & Operations

  • Kubernetes namespace isolation
  • Multi-tenant PaaS architecture
  • RESTful APIs + SDK interfaces
  • Federated learning with differential privacy

Technical Benchmarks

Measured performance characteristics from production deployments.

<100ms
Knowledge graph query latency
<5s
Causal inference processing
<30s
1000-scenario Monte Carlo
<60s
End-to-end decision cycle
>99.5%
Entity resolution accuracy
>90%
Automated anomaly resolution