CaaS Intelligence Core

Sense → Predict → Decide → Act. A multi-agent decision engine that balances safety, flow, revenue, and sustainability—auditable and human-overridable.

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What It Is

The CaaS Intelligence Core is the runtime brain of LaplaceX. It unifies streams and systems, reasons via domain agents (Traffic, Parking, Safety, Energy), negotiates trade-offs, and triggers orchestrated actions. Every decision is grounded on the Knowledge Vault and governed by the Trust & Compliance Layer.

Why It Matters

City-Scale Decisions

Transform thousands of signals and data streams into a single, consistent plan that coordinates across all city systems.

Multi-Objective Optimization

Balance competing KPIs—safety, flow, revenue, and sustainability—with intelligent trade-off negotiation between agents.

Governed Automation

Human oversight with approvals, overrides, and full auditability—automation with accountability at every step.

How It Works

1

Sense

Connectors & streams from all city systems

2

Predict

Short-term forecasts & anomaly detection

3

Propose

Domain agents suggest actions

4

Negotiate

Balance weights, constraints, policies

5

Approve & Act

Execute via Incident Orchestrator

6

Learn

Feedback & post-incident outcomes

Architecture

A layered intelligence architecture that separates data, decision-making, governance, and action

Data Plane

Connectors (SQL/APIs/streams/IoT), schema registry, change data capture

Intelligence Plane

Agent runtime, planner, tool use, vector/graph retrieval

Governance

Trust & Compliance Layer (policies, access, redaction, audit)

Knowledge

Knowledge Vault (entities, lineage, citations)

Action Plane

Incident Orchestrator, webhooks, CAD/WFM/signals/VMS

Key Capabilities

Multi-agent proposals & negotiation with constraints

Predictive scoring (demand, congestion, incident risk)

Policy-guarded actions (human-in-the-loop)

Scenario tuning & what-if simulation

Citations & evidence for every decision

SDK & APIs for embedding into existing ops centers

Deterministic fallbacks when data is incomplete

Real-time monitoring and alerting

Agent Negotiation Simulator

Experience how domain agents balance competing objectives to reach optimal decisions

Objective Weights
40%
30%
15%
15%
Governance Note: Weights are normalized automatically. All proposed actions require approval through the Trust & Compliance Layer before execution.
Recommended Plan
Traffic Agent
Overall Score: 66/100
Proposed Actions
  • Adjust signal plan B
  • +10% green wave corridor
Score Breakdown
Citations: Knowledge Vault entities & forecasts; approvals required via Trust & Compliance Layer.
Agent Proposals Ranked
Traffic
TOP
66/100
Safety72
Flow88
Revenue20
Sustainability55
Parking
65/100
Safety60
Flow70
Revenue85
Sustainability45
Safety
61/100
Safety92
Flow54
Revenue10
Sustainability40
Energy
60/100
Safety58
Flow62
Revenue30
Sustainability90
Execution Preview (After Approval)
Create Incident IO-492 with playbook
Push Signal Plan B (corridor West)
Adjust parking pricing bands downtown (+£0.5)
Notify patrol units & publish driver guidance

Proven Outcomes

↓ 30-40%
Incident response time reduction
Revenue ↑
Outcomes without compromising safety
Zero Conflicts
Fewer conflicts & duplicate dispatches

Use Cases

Mobility & Parking

Coordinate signal timing with dynamic pricing during events. Optimize traffic flow while maximizing parking revenue and maintaining safety standards.

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Public Safety

Multi-agency coordination with built-in guardrails. Balance emergency response needs with traffic management and resource optimization.

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Energy & Facilities

Load shifting with SLA compliance and override capabilities. Optimize energy consumption while maintaining service levels and emergency readiness.

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API Integration

Programmatic access to the intelligence core for decision support and automation

Decision Request
// POST /api/caas/decide
{
  "area": "downtown",
  "horizonMins": 120,
  "objectives": {
    "safety": 0.4,
    "flow": 0.3,
    "revenue": 0.15,
    "sustainability": 0.15
  }
}

// Response
{
  "proposals": [
    {
      "agent": "Traffic",
      "actions": ["Adjust signal plan B", "+10% green wave"],
      "scores": {"safety": 72, "flow": 88, "revenue": 20}
    }
  ],
  "recommendation": {
    "agent": "Traffic",
    "actions": [...],
    "score": 86
  },
  "citations": [...]
}
Decision Execution
// POST /api/caas/execute
{
  "recommendationId": "rec-123",
  "approver": "ops.supervisor"
}

// Response
{
  "ok": true,
  "incident": "IO-492",
  "tasks": [
    {
      "id": "t1",
      "text": "Adjust signal timing",
      "owner": "traffic_ops",
      "status": "pending"
    }
  ],
  "auditTrail": "aud-7829"
}

Frequently Asked Questions

Does it act automatically?

Only with explicit policy approval and human oversight. By default, the system operates in recommend-only mode, requiring manual approval for all actions through the Trust & Compliance Layer.

How are trade-offs configured?

Objective weights can be set per city, zone, or time period using templates and quick toggles. Administrators can create different profiles for normal operations, events, emergencies, and maintenance windows.

Is every decision explainable?

Yes—every decision includes objective scores, constraint explanations, data citations, and a complete audit trail. Users can trace from final action back to source data and reasoning chain.

Run Your City on a Governed AI Core

Transform reactive operations into intelligent, coordinated responses. Experience the power of multi-agent decision-making with complete transparency and human oversight.

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