AI agent model

Looma uses AI to interpret and govern road signals, not to drive the vehicle.

The agent story is strongest when it is operationally specific: agents read signed alerts, cluster incidents, draft briefs, produce pilot evidence, screen counterparties, and enforce approval boundaries through MCP tools and governance logs.

Signal Triage Agent

Input
Recent signed alerts, GPS envelope, alert category, device identity, expiration window.
Output
Clustered incident candidates, duplicate suppression notes, anomaly flags, and operator priority.

Incident Briefing Agent

Input
Alert cluster, route context, event timeline, nearby feed, and operator notes.
Output
A short incident brief that explains what changed, where it changed, and why the operator should care.

Pilot Evidence Agent

Input
Relay events, uptime, message delivery, coverage zones, customer notes, and demo activity.
Output
Investor and customer reports showing product usage, reliability, quality gaps, and pilot readiness.

Integration Agent

Input
Fleet/OEM requirements, API docs, sample payloads, data contracts, and LunaForge impact reports.
Output
Adapter specs, test payloads, integration checklists, and engineering-risk summaries.

Compliance Agent

Input
Customer entity, vendor entity, procurement contact, region, contract notes, and Amliq screening result.
Output
Counterparty risk summary, case reference, reviewer recommendation, and audit trail link.

Governance Agent

Input
Requested MCP call, user role, customer tenant, approval state, policy, and OpenSyber audit context.
Output
Allow, deny, require approval, redact, or escalate with a durable audit record.

Generative AI boundaries

The model writes explanations, reports, specs, and reviews. It does not create unapproved safety outcomes.

Allowed generative work

  • Summarize many alerts into one operational incident brief.
  • Draft investor-ready pilot reports from telemetry and notes.
  • Explain integration risk in plain language for non-technical buyers.
  • Generate adapter specs and test payloads for fleet or city integrations.
  • Redact sensitive details before evidence leaves a tenant boundary.

Blocked positioning

  • No autonomous braking, steering, dispatching, or routing commands.
  • No uncertified claim that a generated summary is a road-safety decision.
  • No agent writes to customer-facing evidence without approval and audit.
  • No compliance screening outside the approved counterparty workflow.

Permission model

MCP is useful because every capability can be named, scoped, logged, and approved.

Scope
Allowed agent
Control
Read signed alertsTriage, briefing, pilot evidence

No approval for internal demo data; tenant approval for customer exports.

Submit alertVehicle adapter and controlled demo tools

Signature required. Agent-generated safety alerts require human review before external use.

Generate incident briefBriefing and pilot agents

No approval for draft. Approval required before customer-facing report export.

Screen counterpartyCompliance agent

Approval required because the output can affect commercial decisions.

Change production relay logicEngineering agent only as reviewer, not deployer

Requires tests, LunaForge impact evidence, maintainer review, and deployment approval.