Curriculum Agent
Drafts aligned learning materials and exercises
Tool: Content generator · Source: Learning management system
Industry
Education AI agent operations
Learning platforms, assessment data, curriculum content and student support
Operational layers in view
Curriculum contentAssessment dataEngagement signalsSupport ticketsProgress dashboard
Curriculum Agent
Assessment Analytics Agent
Early Warning Agent
Feedback Agent
Accessibility Agent
Integrity Agent
Node activity below is a demonstration using sample education 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
Education
- Curriculum Agent
- Assessment Analytics Agent
- Early Warning Agent
- Feedback Agent
- Accessibility Agent
- Integrity Agent
Deliverables planned
Dashboard
Charts
Data tables
Timeline
Recommendations
Pauses for human approval
Educator 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
Education AI agent operations centre
Workflow: Cohort data received → Monitor progress · 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
Assessment Analytics Agent
Finds concept-level gaps across cohorts
Tool: Content generator · Source: Learning management system
Early Warning Agent
Flags students at risk of falling behind
Tool: Content generator · Source: Learning management system
Feedback Agent
Drafts personalised, evidence-based feedback
Tool: Content generator · Source: Learning management system
Workflow canvas
Live activity stream
No activity yet. Start the demo run to watch education 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.
Education specialist agents
Each agent owns one step of the workflow, with its own tools, inputs and approval level.
Autonomous
Curriculum Agent
Drafts aligned learning materials and exercises
- Inputs:
- Learning management system, Assessment records, Attendance and engagement logs
- Tools:
- Content generator, Assessment analyser, Early-warning model
Autonomous
Assessment Analytics Agent
Finds concept-level gaps across cohorts
- Inputs:
- Learning management system, Assessment records, Attendance and engagement logs
- Tools:
- Content generator, Assessment analyser, Early-warning model
Review advised
Early Warning Agent
Flags students at risk of falling behind
- Inputs:
- Learning management system, Assessment records, Attendance and engagement logs
- Tools:
- Content generator, Assessment analyser, Early-warning model
Approval required
Feedback Agent
Drafts personalised, evidence-based feedback
- Inputs:
- Learning management system, Assessment records, Attendance and engagement logs
- Tools:
- Content generator, Assessment analyser, Early-warning model
Autonomous
Accessibility Agent
Adapts materials for accessibility requirements
- Inputs:
- Learning management system, Assessment records, Attendance and engagement logs
- Tools:
- Content generator, Assessment analyser, Early-warning model
Approval required
Integrity Agent
Checks authorship and policy compliance
- Inputs:
- Learning management system, Assessment records, Attendance and engagement logs
- Tools:
- Content generator, Assessment analyser, Early-warning model
Solution blueprint
Operational context
Industry challenges
- Operational data for education 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
- Learning management system
- Assessment records
- Attendance and engagement logs
- Curriculum library
Controlled process
Example workflow
- Cohort data received
- Validate records
- Analyse assessment results
- Identify learning gaps
- Flag at-risk students
Connection points
Integration opportunities
- LMS
- Student information systems
- Content repositories
- Identity 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
- • Learning management system
- • Assessment records
- • Attendance and engagement logs
- • Curriculum library
Integrations
- • LMS
- • Student information systems
- • Content repositories
- • Identity services
Outputs
- • Cohort insight report (PDF, CSV)
- • Intervention plan (PDF)
- • Progress 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 education agent team follows, from trigger to approved deliverable.
Knowledge retrieval workflow
From an ingested corpus to grounded, cited answers with coverage reporting.
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. Ingest documents: Input. Approved documents added to the workspace corpus.
- 2. Validate and de-duplicate: Validation. Check formats, versions and duplicate content.
- 3. Index content: Approved tool. Chunk and embed the corpus for retrieval.
- 4. Answer with citations: AI agent. Answer only from retrieved passages and cite each one.
- 5. Coverage check: Decision. Say clearly when the corpus does not answer the question.
- 6. Cited answer and coverage report: Output. Answer, sources and gaps in the corpus.
Approval gate: none configured. Deliverable: Report.
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 |
|---|---|---|---|---|---|---|---|
| knowledge-base-agent | 6 | No | Report | 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.