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ICDT Core Architecture

The Integrated Cognitive Digital Twin is a backbone for persistent, owner-controlled intelligence. It connects static data to dynamic workflow state, enabling systems that reason rather than just report.

The Core Concept: Twin–Artifact–Lens

The architecture decouples the "truth" of an entity from how it is viewed or acted upon:


System Layers

1. Data Ingestion & Curation

Raw data is never trusted blindly. The ingestion layer validates, normalizes, and semantically tags incoming telemetry before it enters the twin’s memory. This separates "noise" from "signal" at the source.

2. Memory Layer

A persistent, owner-controlled graph that stores not just the current state, but the history of state changes and the decisions that drove them. This enables "time-travel" debugging and auditable chains of custody.

3. Workflow Layer

Awareness of the "job to be done." The system understands where an asset is in its lifecycle (e.g., "Drilling - 12.25in Section"). This allows agents to apply context-specific rules rather than generic thresholds.

4. Agent Orchestration

Specialized, narrow-scope agents (e.g., "Survey QA Agent," "Torque & Drag Monitor") operate autonomously within the twin. A central orchestrator routes information to the right agent and resolves conflicts, preventing agent collision.

5. Governance & Presentation

A rigorous permissioning layer ensures that while the twin holds the total truth, external users (partners, regulators, investors) access only permissible views (Lenses). Use of deterministic logic for safety-critical decisions builds trust that probabilistic AI cannot achieve alone.

Logic Philosophy: Deterministic + Probabilistic

Trust requires predictability. ICDT enforces a strict separation of concerns:

Security & Integration

Owner Control: The architecture is designed for sovereign data ownership. The twin lives in the owner's environment, not a vendor's black box.
Coexistence: ICDT is not a "rip-and-replace" solution. It sits alongside existing SCADA and ERP systems, ingesting their outputs to build higher-order intelligence.

Current Status

Architecture defined. Workflows modeled. Implementation staged.