Everything you need to understand, implement, and optimize MIZ OKI 3.5
Technical guides for every stage of your journey
Deploy your first autonomous decision workflow in under 30 minutes
Complete technical reference for the 7-stage autonomous decision pipeline
How the DCP separates action proposal from authorization
Connect your CRM, ERP, advertising platforms, and data warehouses
Define authorization thresholds, risk limits, and compliance constraints
Track decision performance, audit logs, and system health
Build domain-specific Autonomous Decision Controllers
Optimize causal discovery and confounder detection
Implementation guide for CRYSTALS-Kyber and Dilithium
In-depth analysis and technical foundations
This foundational paper introduces the concept of separating action proposal from authorization in autonomous AI systems. We present the Decision Control Plane architecture and demonstrate how verified autonomy achieves 50-75× improvement in decision velocity while maintaining full auditability.
Traditional knowledge graphs store facts. The TCO-KG stores decisions—proposals, simulations, validations, and outcomes with temporal validity and causal confidence. This paper details the architecture and demonstrates 99.5% entity resolution accuracy.
Meta's advertising algorithms exhibit non-linear, threshold-driven behavior that mirrors ReLU activation functions. This paper presents a practical framework for threshold-aware media buying that reduces Learning Limited incidents by 68%.
As quantum computing advances, today's encryption becomes tomorrow's vulnerability. This paper details our implementation of NIST-standardized post-quantum cryptography for securing autonomous decision systems.
Integrate MIZ OKI 3.5 into your existing systems with our comprehensive REST API. Full OpenAPI specification, SDKs for major languages, and real-time webhooks.
from mizoki import Client # Initialize client client = Client(api_key="your_api_key") # Submit a decision request decision = client.decisions.create( domain="media_buying", action_type="budget_adjustment", parameters={ "campaign_id": "camp_123", "adjustment": "+15%" }, require_simulation=True ) # Check authorization status if decision.status == "authorized": print(f"Executing: {decision.id}")
Submit a new decision for validation and authorization
Retrieve decision status, validation results, and audit trail
Get counterfactual simulation results and comparisons
Query the Temporal-Causal Knowledge Graph
Retrieve immutable audit logs for compliance
Update policy constraints and authorization thresholds
Step-by-step guides from our implementation team
Navigate the dashboard and submit your first autonomous decision
Define authorization thresholds, risk limits, and escalation rules
Integrate CRM, advertising platforms, and data warehouses
How Planner, Verifier, Risk, and Policy agents evaluate decisions
Apply threshold-aware strategies to your Meta campaigns
Generate compliance reports and access decision audit trails