System Architecture

Substrate Guarantees

In contested environments, a system is only trustworthy if its substrate cannot drift.

System Overview

ARIA is built on a layered architecture designed for resilience, autonomy, and deterministic behavior in mission-critical environments. The system comprises four primary layers:

Figure 1: High-level system architecture showing layer interactions

Context Kernel v3

The Context Kernel is the heart of ARIA's autonomous intelligence. Version 3 introduces significant improvements in state management, recovery paths, and multi-agent coordination.

Core Responsibilities

• State Management: Maintains consistent system state across all agents with durability guarantees
• Request Routing: Classifies and routes requests to appropriate agents or models
• Context Preservation: Maintains context through agent handoffs and recovery events
• Circuit Breaking: Prevents cascading failures with automatic backoff (5 failure threshold, 60s reset)

Memory Bus Architecture

The Memory Bus provides a high-performance, transactional communication layer between agents. It ensures data consistency while maintaining low latency under high throughput conditions.

Key Features

Persistence Scope: Persistence guarantees vary by deployment profile and operator configuration. Durability is governed by policy, not architecture.

Performance Characteristics

Governance & Control

ARIA's governance layer enforces policy decisions through a weighted voting system. All high-risk operations require board approval, ensuring operators maintain control of autonomous behavior.

Governance Board Structure

Proposal Types & Requirements

Governance Features

Policy Execution

High-risk actions (policy changes, autonomy level changes) require explicit board approval. Low-risk actions (scale-up, mesh admission) execute with CTO approval but remain auditable. All decisions enforce ROE compliance before execution.

Agent Orchestration & Lifecycle Management

ARIA employs a multi-agent architecture where specialized agents handle distinct operational domains. The AgentManager controls all agent lifecycle operations. Agents do not self-spawn; all creation requires supervisor authorization and is tracked in the governance audit log.

Agent Lifecycle

Figure 2: Agent state transitions

Agent Types

Agent Communication Flow

Figure 2b: Agent communication and orchestration flow

Agent Coordination Patterns

Mesh Admission Control

Discovery does not equal participation. In ARIA's mesh architecture, nodes transition through carefully controlled states before joining active operation. Operators maintain explicit control over node admission and can quarantine or evict nodes as needed.

Node State Transitions

Admission Control Features

Workload Distribution

ARIA tracks workload across active nodes. Distribution respects node capability declarations and current load. When a node becomes DEGRADED (80% capacity), it stops accepting new work but completes in-progress tasks. When a node drops to OFFLINE, work is redistributed immediately.

Platform Support & Deployment Profiles

ARIA is deployed via platform-specific profiles that handle operating system differences. Each profile runs the same core ARIA OS backend with platform-specific configuration and dependencies.

Hardware Capability Awareness

This layer governs feature availability and policy enforcement based on hardware capabilities, not performance normalization. Operators retain control over which subsystems are active on each platform.

Figure 3: Platform-specific deployment profiles (macOS, Windows, Linux)

Supported Operating Systems

Deployment Requirements

Minimum and recommended resources for ARIA OS deployment across platforms.

Platform Capabilities

ARIA core functionality is consistent across platforms. Platform-specific adapters handle OS differences.

Pre-LLM Compliance Layer

The compliance layer enforces policy constraints before model inference, ensuring that unsafe operations are blocked at the kernel level.

Figure 4: Pre-LLM compliance blocking flow

Policy Categories

• Safety Constraints: Physical safety boundaries, forbidden actions
• Mission Parameters: Operational limits, ROE compliance
• Resource Limits: Memory caps, compute quotas, time bounds
• Access Control: Agent permissions, data classification

Autonomous Recovery System

ARIA implements autonomous healing loops that detect, isolate, and recover from failures without human intervention.

Figure 5: Autonomous recovery loop architecture

Recovery Phases

Recovery Time Objectives

Ownership Model: Recovery decisions originate in the kernel; supervisor processes enforce process-level restarts and containment. This separation keeps control and execution distinct.

Operational UI

Below is a live view of ARIA's operational console. These interfaces are real and fully functional.

Evidence Management

ARIA automatically captures, hashes, classifies, and seals operational evidence with chain-of-custody integrity. Every action, override, and decision is logged and reviewable.

Mission Control

Plan, supervise, and monitor autonomous missions with structured briefs, checkpoints, and outcome tracking. Built for real-time operations.

Agent Supervision

Monitor each agent's health, approvals, error rate, and rationale. Provides complete oversight for human operators.

Decision & Learning Agents

ARIA shows behavior, not guesses. Every recommendation, plan change, and pattern consolidation is transparent and auditable.

Autonomy Control

A mission-bound autonomy governor with clear capabilities, restrictions, and audit logs for every level. Operators stay in control at all times.

Autonomy Levels & Decision Authority

ARIA supports structured autonomy levels ranging from L0 (Manual) to L4 (Mission). Escalation or reduction of autonomy is tightly governed, requiring explicit operator or administrator action, written justification, and full audit logging. This ensures compliance with Rules of Engagement (ROE), mission parameters, and safety boundaries across all operating environments.

Validation and Research Posture

Simulation and synthetic benchmarks establish baseline behavior but plateau when stress conditions involve hardware-constrained dynamics, sustained network degradation, or audit integrity under prolonged failure injection.

Dedicated hardware validation is conducted separately to examine these limits under controlled conditions. See current validation research →