Asset Monitoring Agent
Watches SCADA and IoT streams for drift on rotating and static assets
Tool: Anomaly detection model · Source: SCADA historian
Industry
Oil and gas AI agent operations
Upstream field, pipeline network and refinery assets under continuous monitoring
Operational layers in view
GIS pipeline networkIoT pressure and vibrationDigital twinSafety alertsProduction data
Asset Monitoring Agent
Pipeline Integrity Agent
Predictive Maintenance Agent
Safety and Risk Agent
Environmental Agent
Production Analytics Agent
Node activity below is a demonstration using sample oil and gas 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
Oil and gas
- Asset Monitoring Agent
- Pipeline Integrity Agent
- Predictive Maintenance Agent
- Safety and Risk Agent
- Environmental Agent
- Production Analytics Agent
Deliverables planned
Interactive map
Dashboard
Charts
Data tables
Timeline
Pauses for human approval
Human engineering 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
Oil and gas AI agent operations centre
Workflow: Sensor and GIS data received → Monitor asset · 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
Pipeline Integrity Agent
Correlates pressure, flow and inspection history along each segment
Tool: Anomaly detection model · Source: SCADA historian
Predictive Maintenance Agent
Scores failure probability and proposes intervention windows
Tool: Anomaly detection model · Source: SCADA historian
Safety and Risk Agent
Assesses personnel and process-safety exposure of each finding
Tool: Anomaly detection model · Source: SCADA historian
Workflow canvas
Live activity stream
No activity yet. Start the demo run to watch oil and gas 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.
Oil and gas specialist agents
Each agent owns one step of the workflow, with its own tools, inputs and approval level.
Autonomous
Asset Monitoring Agent
Watches SCADA and IoT streams for drift on rotating and static assets
- Inputs:
- SCADA historian, IoT sensor stream, GIS pipeline layers
- Tools:
- Anomaly detection model, GeoAI locator, Failure-risk model
Autonomous
Pipeline Integrity Agent
Correlates pressure, flow and inspection history along each segment
- Inputs:
- SCADA historian, IoT sensor stream, GIS pipeline layers
- Tools:
- Anomaly detection model, GeoAI locator, Failure-risk model
Review advised
Predictive Maintenance Agent
Scores failure probability and proposes intervention windows
- Inputs:
- SCADA historian, IoT sensor stream, GIS pipeline layers
- Tools:
- Anomaly detection model, GeoAI locator, Failure-risk model
Approval required
Safety and Risk Agent
Assesses personnel and process-safety exposure of each finding
- Inputs:
- SCADA historian, IoT sensor stream, GIS pipeline layers
- Tools:
- Anomaly detection model, GeoAI locator, Failure-risk model
Autonomous
Environmental Agent
Estimates spill, emission and sensitive-receptor impact
- Inputs:
- SCADA historian, IoT sensor stream, GIS pipeline layers
- Tools:
- Anomaly detection model, GeoAI locator, Failure-risk model
Autonomous
Production Analytics Agent
Quantifies deferred production for each scenario
- Inputs:
- SCADA historian, IoT sensor stream, GIS pipeline layers
- Tools:
- Anomaly detection model, GeoAI locator, Failure-risk model
Autonomous
GeoAI Agent
Locates assets, buffers exposure zones and maps affected sections
- Inputs:
- SCADA historian, IoT sensor stream, GIS pipeline layers
- Tools:
- Anomaly detection model, GeoAI locator, Failure-risk model
Autonomous
Compliance Agent
Checks findings against regulator and internal integrity standards
- Inputs:
- SCADA historian, IoT sensor stream, GIS pipeline layers
- Tools:
- Anomaly detection model, GeoAI locator, Failure-risk model
Solution blueprint
Operational context
Industry challenges
- Sensor, GIS and maintenance data live in separate systems
- Integrity anomalies are found after production impact
- Manual root-cause analysis across decades of records
- Heavy HSE and environmental reporting overhead
Solution patterns
Recommended AI solutions
- Continuous anomaly detection across SCADA and IoT streams
- GeoAI location of the affected pipeline segment and exposure
- Predictive failure-risk scoring with maintenance recommendations
- Approval-gated work-order drafting into the CMMS
Data readiness
Required data
- SCADA historian
- IoT sensor stream
- GIS pipeline layers
- Maintenance records
- HSE incident log
Controlled process
Example workflow
- Sensor and GIS data received
- Validate data quality
- Detect anomaly
- Locate affected asset
- Predict failure risk
Connection points
Integration opportunities
- SCADA / historian
- CMMS (SAP PM, Maximo)
- GIS server
- HSE reporting
Measure after baseline
Expected outcomes
- Unplanned downtime
- Anomaly detection lead time
- Integrity report effort
- HSE findings closed on time
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
- • SCADA historian
- • IoT sensor stream
- • GIS pipeline layers
- • Maintenance records
- • HSE incident log
Integrations
- • SCADA / historian
- • CMMS (SAP PM, Maximo)
- • GIS server
- • HSE reporting
Outputs
- • Asset risk map (GeoJSON, PDF)
- • Pipeline anomaly report (PDF, CSV)
- • Predictive maintenance recommendation (PDF, JSON)
- • Safety and environmental summary (PDF)
- • Management dashboard (Dashboard)
Business outcomes to baseline and measure
Unplanned downtime
Target and measurement method agreed during discovery.
Anomaly detection lead time
Target and measurement method agreed during discovery.
Integrity report effort
Target and measurement method agreed during discovery.
HSE findings closed on time
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 oil and gas agent team follows, from trigger to approved deliverable.
Drawing and specification review workflow
From an uploaded drawing to an engineer-reviewed annotated set and schedule.
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. Upload drawing or specification: Input. PDF drawing, DWG export or specification document.
- 2. File validation: Validation. Check sheet count, scale references and legibility.
- 3. OCR and classification: Approved tool. Read title blocks, notes and schedules.
- 4. Extract elements: AI agent. List elements, quantities and referenced standards.
- 5. Validate dimensions: Validation. Cross-check dimensions and quantities against the schedule.
- 6. Detect issues: Decision. Flag conflicts, missing details and standard deviations.
- 7. Engineer review: Human approval. A qualified engineer approves every flagged item. Human approval required.
- 8. Annotated drawing and schedule: Output. Annotated PDF, element schedule and issue list.
Approval gate: Engineer review. Deliverable: Annotated document.
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 |
|---|---|---|---|---|---|---|---|
| engineering-document-agent | 8 | Yes. Policy: always | Annotated document | Available | Available (signed in) | Available via Studio · requires ai_provider | 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.