Multi-Agent Architecture

The architecture

Lifeverse AI doesn't deploy a single AI assistant. It deploys an AI organization — multiple agents with defined roles, communication channels, and shared knowledge.

Agent structure

Each agent has:

• Role — defined responsibilities and expertise (Technical Manager, Superintendent, etc.)
• Authority — scope of autonomous decision-making
• Memory — 14-table SQLite brain with persistent state
• Communication — A2A messaging for inter-agent collaboration
• Skills — domain-specific tools and commands
• Schedule — daemon-managed execution frequency

Team organization

flowchart TD
    CEO[CEO Agent]
    CEO --> TM[Technical Manager]
    CEO --> OM[Operations Manager]
    CEO --> CS[Customer Success]
    CEO --> DL[Dev Lead]
    TM --> S1[Superintendent 1]
    TM --> S2[Superintendent 2]
    S1 --> E1[Engineer 1]
    S2 --> E2[Engineer 2]
    OM --> CH[Charterer]
    DL --> D1[Developer 1]

Communication patterns

Direct messaging

Agent-to-agent for specific requests, status updates, and task delegation.

Team broadcasts

One-to-many within a team for announcements and coordination.

Escalation chains

Automatic routing to higher authority when a decision exceeds an agent's scope.

Urgent shouts

Priority bypass for critical external inputs (e.g., emergency notifications).

Memory persistence

The sleep/wake model ensures agents never lose context:

1. Sleep: Before session ends, agent saves state to SQLite
2. Wake: On next session, agent loads identity + recent context + pending items
3. Layered injection: Only relevant memory is loaded, keeping context windows efficient

Organizational learning

Individual agent experiences feed into a shared knowledge library:

Agent resolves issue → Reflects on resolution → Knowledge Library updated → All agents benefit

The organization accumulates institutional knowledge over time — incident patterns, maintenance best practices, regulatory updates, vendor insights.

Design principles