💠Economic Layer

Reference Architecture

IMMT is implemented as a model-agnostic, modular reference architecture designed to support multiple AI models, deployment environments, and regulatory contexts. The architecture separates cognition, policy enforcement, execution, and observability to ensure scalability, safety, and long-term maintainability.

Rather than binding intelligence to a single foundation model, IMMT treats models as replaceable reasoning engines operating within a fixed governance and execution framework.

Interface Layer

The Interface Layer serves as the primary point of interaction between the Owner and IMMT, supporting multimodal communication and control.

Responsibilities:

• Natural language, voice, and text-based interaction

• Multimodal input/output (text, audio, documents, structured data)

• Relationship mode selection and switching

• Intervention controls (frequency, intensity, escalation thresholds)

• Consent and permission prompts for sensitive actions

This layer abstracts interaction logic from internal reasoning, allowing IMMT to adapt its behavior without exposing system complexity to the user.

Agent Runtime

The Agent Runtime is the core cognitive execution environment where decision-making and task execution occur. It coordinates multiple specialized components:

Planner

• Translates goals into structured plans

• Performs task decomposition and prioritization

• Considers constraints such as time, cost, and risk

• Generates alternative strategies when trade-offs exist

Executor

• Orchestrates tool calls and workflow execution

• Manages retries, fallbacks, and partial failures

• Interfaces with external systems and APIs

Critic / Verifier

• Evaluates planned and executed actions

• Detects logical errors, inconsistencies, or policy violations

• Provides self-correction signals before execution when possible

Memory Manager

• Routes information between working, episodic, and long-term memory

• Enforces retention, decay, and redaction rules

• Prevents unbounded memory growth or leakage

The Agent Runtime operates continuously but remains bounded by policy and approval constraints.

Policy & Safety Engine

The Policy & Safety Engine is the central governance layer of IMMT, enforcing boundaries on autonomy and execution.

Core Functions:

• Definition and enforcement of autonomy levels

• Taboo and boundary rule evaluation

• Risk scoring and threshold enforcement

• Approval gates for sensitive or irreversible actions

• Emergency stops and capability revocation

All plans and actions generated by the Agent Runtime must pass through this engine before execution. This ensures that intelligence does not equate to unchecked authority.

Data & Memory Layer

The Data & Memory Layer stores and manages all Owner-related and system-derived information while prioritizing privacy, security, and controllability.

Memory Types:

• Profile Memory: Stable preferences, goals, constraints, and permissions

• Episodic Memory: Time-bound experiences, decisions, and outcomes

• Working Memory: Short-term context for active reasoning tasks

Security Features

• Encryption at rest and in transit

• Selective redaction and forgetting

• Owner-controlled memory visibility and deletion

• Contextual access control per subsystem

This design ensures personalization without permanent overexposure.

Economic Agent Module (Optional)

The Economic Agent Module enables IMMT to participate in financial and economic workflows under strict permissioning.

Capabilities:

• Wallet management and key abstraction

• Transaction construction and submission

• Integration with DeFi, TradFi, or payment APIs

• Risk evaluation and exposure management

• Simulation and pre-execution validation

This module is optional and can be fully disabled or sandboxed, allowing IMMT to function safely in non-financial contexts.

Audit & Observability Layer

The Audit & Observability Layer provides transparency, accountability, and debuggability across the entire system.

Components:

• Append-only, tamper-resistant execution logs

• Policy state snapshots at decision time

• Action–outcome correlation tracking

• Monitoring dashboards for system health and behavior

• Forensic replay of decision paths

This layer enables:

• Post-incident analysis

• Regulatory and compliance review

• Continuous system improvement

• Owner trust through explainability

Architectural Design Principles (Implicit but Strong)

• Model-agnostic: AI models can be swapped without redesigning governance

• Separation of concerns: Intelligence ≠ authority ≠ execution

• Progressive trust: Capability expansion follows demonstrated reliability

• Fail-safe defaults: When uncertain, IMMT escalates or abstains