Urban Intelligence Agent
Fuses city layers into one operational picture
Tool: Twin query API · Source: City digital twin
Industry
Smart cities AI agent operations
3D city digital twin with traffic, utilities, environment and public-service layers
Operational layers in view
3D city modelTraffic flowUtility networkEnvironmental sensorsService indicatorsIncidents
Urban Intelligence Agent
Traffic Agent
Utility Agent
Environmental Agent
Digital Twin Agent
Emergency Support Agent
Node activity below is a demonstration using sample smart cities data.
Ask the live agents
Describe the problem in your own words. We classify the industry, assign specialist agents, and you watch the workflow run and produce the visual deliverables — maps, dashboards, charts, tables and reports — instead of one block of text.
Demo mode uses clearly labelled sample data — never presented as verified.
Assigned specialists
Smart cities
- Urban Intelligence Agent
- Traffic Agent
- Utility Agent
- Environmental Agent
- Digital Twin Agent
- Emergency Support Agent
Deliverables planned
Interactive map
Dashboard
Charts
Workflow diagram
Data tables
Pauses for human approval
Operator approval
Workflow the run follows
- 1. User request
- 2. Requirement validation
- 3. Industry classification
- 4. Specialist agent selection
- 5. Data and file collection
- 6. Tool authorisation
- 7. Execution plan
- 8. Multi-agent collaboration
- 9. Analysis or generation
- 10. Quality validation
- 11. Human approval
- 12. Visual deliverables
- 13. Monitoring and audit
Demo
Smart cities AI agent operations centre
Workflow: City sensor feeds received → Monitor city KPIs · Run pending · started — · elapsed 0s
Active agents
0
Tasks running
0
Completed steps
0/9
Tool calls
0
Human approvals
1
Warnings
0
Success rate
0%
Est. completion
36s
Active agents
Pending
Traffic Agent
Detects congestion and proposes signal or routing responses
Tool: Twin query API · Source: City digital twin
Utility Agent
Watches water, power and waste network performance
Tool: Twin query API · Source: City digital twin
Environmental Agent
Tracks air, noise and heat exposure by district
Tool: Twin query API · Source: City digital twin
Workflow canvas
Live activity stream
No activity yet. Start the demo run to watch smart cities agents work through the workflow with sample data.
Demonstration using sample data — no real client operation is shown. Sign in and connect an approved AI provider and authorised industry data source to activate live execution.
Smart cities specialist agents
Each agent owns one step of the workflow, with its own tools, inputs and approval level.
Autonomous
Urban Intelligence Agent
Fuses city layers into one operational picture
- Inputs:
- City digital twin, Traffic counters and cameras, Air-quality sensors
- Tools:
- Twin query API, Traffic model, Environmental index
Review advised
Traffic Agent
Detects congestion and proposes signal or routing responses
- Inputs:
- City digital twin, Traffic counters and cameras, Air-quality sensors
- Tools:
- Twin query API, Traffic model, Environmental index
Autonomous
Utility Agent
Watches water, power and waste network performance
- Inputs:
- City digital twin, Traffic counters and cameras, Air-quality sensors
- Tools:
- Twin query API, Traffic model, Environmental index
Autonomous
Environmental Agent
Tracks air, noise and heat exposure by district
- Inputs:
- City digital twin, Traffic counters and cameras, Air-quality sensors
- Tools:
- Twin query API, Traffic model, Environmental index
Autonomous
Digital Twin Agent
Runs what-if scenarios against the 3D model
- Inputs:
- City digital twin, Traffic counters and cameras, Air-quality sensors
- Tools:
- Twin query API, Traffic model, Environmental index
Approval required
Emergency Support Agent
Prepares incident response options for operators
- Inputs:
- City digital twin, Traffic counters and cameras, Air-quality sensors
- Tools:
- Twin query API, Traffic model, Environmental index
Solution blueprint
Operational context
Industry challenges
- Operational data for smart cities is spread across disconnected systems
- Analysis-heavy work is repeated manually every cycle
- Risks and exceptions are found late
- Reporting and audit overhead grows with volume
Solution patterns
Recommended AI solutions
- Specialist agents that own one step of the workflow each
- Grounded retrieval over your permitted systems only
- Predictive scoring with explainable reasons
- Approval-gated execution into your systems of record
Data readiness
Required data
- City digital twin
- Traffic counters and cameras
- Air-quality sensors
- Service request tickets
Controlled process
Example workflow
- City sensor feeds received
- Validate feeds
- Detect incident
- Locate in digital twin
- Assess citizen impact
Connection points
Integration opportunities
- City GIS and twin
- Traffic management
- 311 / service desk
- Emergency services
Measure after baseline
Expected outcomes
- Analysis cycle time
- Manual review effort
- Decision auditability
- Earlier risk detection
Governance controls
Security and compliance
- Row-level security scopes every read to the requesting organisation
- Retrieval is limited to documents the user may already open
- Every agent action, tool call and approval is written to the audit log
- Sensitive steps pause until a named human approves them
Inputs, integrations and outputs
Inputs
- • City digital twin
- • Traffic counters and cameras
- • Air-quality sensors
- • Service request tickets
Integrations
- • City GIS and twin
- • Traffic management
- • 311 / service desk
- • Emergency services
Outputs
- • Incident impact map (GeoJSON, PDF)
- • Response option comparison (PDF, CSV)
- • City operations dashboard (Dashboard)
Business outcomes to baseline and measure
Analysis cycle time
Target and measurement method agreed during discovery.
Manual review effort
Target and measurement method agreed during discovery.
Decision auditability
Target and measurement method agreed during discovery.
Earlier risk detection
Target and measurement method agreed during discovery.
No performance result is promised before a baseline is measured. Targets are agreed for each engagement and reported against real delivery data.
How This AI Agent Works
The process the smart cities agent team follows, from trigger to approved deliverable.
Earth observation workflow
From a study area and date range to a specialist-reviewed map and chart set.
Workflow preview
Saved with the run so the recorded execution trail shows what was asked. Demonstration runs use sample data. They do not touch your systems.
Public visitors can preview the structure and inspect every node. Labelled demonstrations and live runs require a signed-in account with configured providers.
72%
Workflow summary as a text list
- 1. Define study area and dates: Input. Draw or upload the area of interest and the observation window.
- 2. Discover imagery: Approved tool. Search the approved satellite archives for matching scenes.
- 3. Filter cloud coverage: Validation. Drop scenes above the cloud threshold and report coverage.
- 4. Preprocess imagery: Approved tool. Mask, mosaic and reproject the retained scenes.
- 5. Run analysis: AI agent. Compute indices or classification over the prepared stack.
- 6. Validate results: Validation. Check against reference samples and report accuracy.
- 7. Specialist review: Human approval. A remote-sensing specialist approves before release. Human approval required.
- 8. Map and charts: Output. Result layer, time-series charts and export package.
Approval gate: Specialist review. Deliverable: Map.
Workflow audit for this page
Structure only. Run a demonstration to include events.
| Template ID | Nodes | Approval node | Output type | Preview | Demo | Live | Mobile test |
|---|---|---|---|---|---|---|---|
| earth-observation-workflow | 8 | Yes. Policy: always | Map | Available | Available (signed in) | Available via Studio · requires ai_provider, python_service, earth_engine | Verified at 390px |
Demonstration runs are stored and replayed from recorded events. Live runs execute server-side in the Studio and only start when the listed integrations report healthy.
Relevant case study
Illustrative case study
Wastewater network GIS for a national water utility
A wastewater network needed utility-GIS structuring, mapping and quality-controlled network intelligence for daily operations.
View all case studiesReady to scope your AI initiative?
Share your objective and constraints. A specialist reviews every request before any recommendation is issued.