Industry

Energy AI agent operations

Generation assets, grid balancing and market signals under live monitoring

Operational layers in view
Generation telemetryGrid loadWeather forecastMarket pricesAsset health
Generation Forecast Agent
Grid Balancing Agent
Asset Health Agent
Market Agent
Outage Planning Agent
Compliance Agent

Node activity below is a demonstration using sample energy 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

Energy

  • Generation Forecast Agent
  • Grid Balancing Agent
  • Asset Health Agent
  • Market Agent
  • Outage Planning Agent
  • Compliance Agent

Deliverables planned

Interactive map
Dashboard
Charts
Data tables

Pauses for human approval

Trading desk approval

Workflow the run follows
  1. 1. User request
  2. 2. Requirement validation
  3. 3. Industry classification
  4. 4. Specialist agent selection
  5. 5. Data and file collection
  6. 6. Tool authorisation
  7. 7. Execution plan
  8. 8. Multi-agent collaboration
  9. 9. Analysis or generation
  10. 10. Quality validation
  11. 11. Human approval
  12. 12. Visual deliverables
  13. 13. Monitoring and audit
Demo

Energy AI agent operations centre

Workflow: Telemetry and forecasts received → Monitor performance · Run pending · started — · elapsed 0s

Sign in for live mode

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

Generation Forecast Agent

Pending

Projects output from wind, solar and thermal assets

Tool: Generation forecast · Source: Turbine and inverter telemetry

Grid Balancing Agent

Pending

Recommends dispatch to match demand

Tool: Generation forecast · Source: Turbine and inverter telemetry

Asset Health Agent

Pending

Scores turbine and inverter degradation

Tool: Generation forecast · Source: Turbine and inverter telemetry

Market Agent

Pending

Evaluates price exposure of each dispatch option

Tool: Generation forecast · Source: Turbine and inverter telemetry

Workflow canvas

Live activity stream

No activity yet. Start the demo run to watch energy 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.

Energy specialist agents

Each agent owns one step of the workflow, with its own tools, inputs and approval level.

Autonomous

Generation Forecast Agent

Projects output from wind, solar and thermal assets

Inputs:
Turbine and inverter telemetry, Grid load data, Weather forecasts
Tools:
Generation forecast, Load balancing model, Asset health scoring
Review advised

Grid Balancing Agent

Recommends dispatch to match demand

Inputs:
Turbine and inverter telemetry, Grid load data, Weather forecasts
Tools:
Generation forecast, Load balancing model, Asset health scoring
Autonomous

Asset Health Agent

Scores turbine and inverter degradation

Inputs:
Turbine and inverter telemetry, Grid load data, Weather forecasts
Tools:
Generation forecast, Load balancing model, Asset health scoring
Approval required

Market Agent

Evaluates price exposure of each dispatch option

Inputs:
Turbine and inverter telemetry, Grid load data, Weather forecasts
Tools:
Generation forecast, Load balancing model, Asset health scoring
Autonomous

Outage Planning Agent

Places maintenance windows at least-cost periods

Inputs:
Turbine and inverter telemetry, Grid load data, Weather forecasts
Tools:
Generation forecast, Load balancing model, Asset health scoring
Autonomous

Compliance Agent

Checks grid-code and reporting obligations

Inputs:
Turbine and inverter telemetry, Grid load data, Weather forecasts
Tools:
Generation forecast, Load balancing model, Asset health scoring

Solution blueprint

Operational context

Industry challenges

  • Operational data for energy 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

  • Turbine and inverter telemetry
  • Grid load data
  • Weather forecasts
  • Market price feeds
Controlled process

Example workflow

  • Telemetry and forecasts received
  • Validate feeds
  • Forecast generation
  • Assess asset health
  • Evaluate dispatch options
Connection points

Integration opportunities

  • SCADA
  • Energy trading systems
  • Asset management
  • Weather providers
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

  • • Turbine and inverter telemetry
  • • Grid load data
  • • Weather forecasts
  • • Market price feeds

Integrations

  • • SCADA
  • • Energy trading systems
  • • Asset management
  • • Weather providers

Outputs

  • • Dispatch recommendation (PDF, CSV)
  • • Asset health report (PDF)
  • • Performance 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 energy agent team follows, from trigger to approved deliverable.

Dataset profiling and analysis workflow

From an uploaded dataset to validated charts and a dashboard-ready report.

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.

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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. 1. Upload dataset: Input. CSV, Excel or table extract uploaded to the workspace.
  2. 2. Validate schema: Validation. Check columns, types, keys and row counts.
  3. 3. Profile data: Approved tool. Compute distributions, nulls, duplicates and outliers.
  4. 4. Clean approved issues only: Decision. Fixes outside the approved rule set are proposed, not applied.
  5. 5. Analyse: AI agent. Answer the stated question and record the method.
  6. 6. Generate charts: Approved tool. Pick the chart type that matches each finding.
  7. 7. Validate metrics: Validation. Recompute headline metrics before publishing.
  8. 8. Dashboard and report: Output. Quality report, charts and cleaned dataset export.

Approval gate: none configured. Deliverable: Dashboard.

Workflow audit for this page
Structure only. Run a demonstration to include events.
Workflow audit: template identifier, node count, approval node, output type, available execution modes and mobile test status.
Template IDNodesApproval nodeOutput typePreviewDemoLiveMobile test
data-quality-agent8NoDashboardAvailableAvailable (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 studies

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