Data lifecycle
Identify authoritative records, history requirements, update frequency, retention, and the consequences of stale information. Preserve source event time and ingestion time where ordering matters.
INDUSTRY ARCHITECTURE / Education
Education architecture
A connected reference design spanning domain information, data engineering, AI services, agent execution, and governance.
DOMAIN DESIGN DECISION
The business context
shapes the architecture.
Course requirements change by academic period, and public guidance must be separated from restricted student information.
Version published catalogs, identify the academic period, preserve policy authority, and exclude restricted records from public retrieval.
Start with student service requests rather than attempting to automate the whole industry at once. Define a bounded request, the evidence required to answer it, and the person accountable for accepting the result.
DATA → AI → AGENTIC AIDOMAIN CONTEXT: Course / Program / Published policy / Service enquiry
Data architecture
- 01
Domain sources
Course catalogs, published policies, administrative requests. Identify the system of record, owner, refresh rate, and approved purpose for each source.
- 02
Ingestion & contracts
Batch, change-data capture, events, and document intake as appropriate. Validate schema, identity keys, event time, and rejected records.
- 03
Storage & transformation
Separate raw evidence, validated records, and curated domain products. Preserve version history and reconciliation results.
- 04
Semantic serving
Course · Program · Published policy · Service enquiry. Publish agreed definitions, access rules, and reusable interfaces for analytics and AI.
AI architecture
- 01
Approved information
Use curated domain products and approved documents. Preserve metadata, effective dates, and access scope.
- 02
Retrieval & features
Build permission-aware retrieval indexes or model features for the specific task. Test relevance and freshness independently.
- 03
Model & evaluation
Evaluate candidate methods on representative domain examples. Check groundedness, completeness, uncertainty, cost, and failure behavior.
- 04
Experience & feedback
Serve sourced drafts or scored signals through a review experience. Capture feedback without silently retraining or widening data access.
Agentic AI architecture
- 01
Task & identity
Record the initiating user, allowed purpose, task boundary, and completion criteria.
- 02
Reasoning & state
Student services agent and Curriculum knowledge agent. Keep task state, retrieved evidence, and unresolved questions traceable.
- 03
Scoped enterprise tools
Expose typed, permission-checked search, lookup, and draft tools. Validate arguments and handle retry or timeout safely.
- 04
Approval & audit
Admissions, grading, eligibility, and interventions remain human decisions. Record tool calls, reviewer decisions, and the accepted result under an agreed retention policy.
Shared governance & operationsOwnership · classification · lineage · access · quality · retention · evaluation · observability · incident response · cost management
Implementation decisions
to resolve before build.
Serving & integration
Choose batch, event, API, or document access based on workload needs. Separate read-only retrieval from tools that alter enterprise records. Define timeouts, retries, and recovery ownership.
Operational evidence
Measure correct-period retrieval, citation accuracy, public/restricted separation, and staff correction frequency. Keep versioned evaluation cases, end-to-end traces, and reviewer feedback so changes can be assessed against an agreed baseline.
CASE
A cited student-services knowledge workflow
See the proposed architecture applied to a focused workflow.
Read more ↗PRODUCT BLUEPRINT
Learning operations workspace
Help staff navigate institutional knowledge and administrative workflows.
Read more ↗INDUSTRY HUB
Education
Explore domain workflows, products, agents, and related knowledge.
Read more ↗A FOCUSED CONVERSATION
Connect the architecture
to your priorities.
Discuss the domain, sources, controls, and outcomes that would define a useful first engagement.
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