The Feature
Build an AI-assisted completion assurance layer for the client's LMS estate. The feature watches learner completion events across LMS, SCORM packages, xAPI/LRS statements, LTI launches, and reporting exports. When completion status looks wrong or delayed, it flags the anomaly, suggests likely cause, and routes the right remediation path.
Why This Feature Fits
- Completion reporting is a common LMS failure point across SCORM, xAPI, cmi5 and LTI boundaries.
- The client has a large delivery footprint with Anitech, client engineering, DevOps and TechOps involved.
- The feature creates visible value without replacing the LMS core.
- It is ideal for AI-native work because signal detection, explanation and routing can be agent-assisted while humans retain approval and accountability.
The Bet Contract
The contract stays short. Supporting analysis can be generated by agents, but the commitment itself must remain readable.
End-to-End BetFlow OS Loop
The feature moves through the five BetFlow OS steps: Sense, Commit, Compose, Prove, Reallocate.
Sense
Agents detect repeated tickets and meeting notes around learners completing courses but reports not updating. Telemetry shows spikes after SCORM package updates and LTI launches.
Commit
Leadership commits a four-week bet to reduce escalations. The accepted outcome is anomaly detection and remediation routing, not automatic record correction.
Compose
The workcell receives bounded access to LMS logs, SCORM runtime records, LRS/xAPI statements, support tickets, reporting exports and test environments.
Prove
The workcell proves detection accuracy on historical cases, validates explanations with support/admin users, and runs regression checks against known content-package edge cases.
Reallocate
If proof is strong, ship as admin-only beta. If weak, reduce to SCORM completion anomalies and re-bet xAPI/LTI coverage separately.
The Workcell
The workcell is temporary and outcome-specific. People, agents, systems and proof standards are composed around this bet.
Outcome Owner
- Confirms value and appetite
- Accepts admin workflow
- Owns client sponsor narrative
System Owner
- Maps LMS, LRS, SCORM and LTI data paths
- Controls architecture fit
- Approves integration approach
Workcell Lead
- Coordinates humans and agents
- Tracks appetite burn
- Escalates blockers and scope cuts
Control Owner
- Approves data boundaries
- Checks AI access and auditability
- Owns proof tier requirements
AI Infrastructure Used
AI is not a special role. It is the operating fabric around the workcell.
Proof Record
Because this is high-risk LMS data, proof must be stronger than a demo.
Value Proof
- Detects at least 80% of known historical completion anomalies in the test set.
- Support/admin users confirm explanations are understandable and actionable.
- Projected escalation reduction is accepted by Outcome Owner.
Risk Proof
- No automated write-back to learner records in v1.
- Data access is read-only and scoped through the context gateway.
- Security review confirms no leakage of learner PII into unmanaged AI context.
Release Proof
- Admin-only beta flag is available.
- Monitoring shows false-positive and false-negative rates.
- Rollback removes anomaly panel without affecting LMS source records.
What Gets Cut If Appetite Is Threatened
The circuit breaker is practical. Scope reduces before appetite expands.
Cut First
- xAPI/LTI anomaly coverage beyond the top known patterns.
- Automated remediation recommendations for edge cases.
- Full manager dashboard view.
- Bulk export and advanced analytics.
Protect
- SCORM completion anomaly detection.
- Admin explanation panel.
- Read-only data safety.
- Evidence record and monitoring.
Learning And Reallocation
The bet should leave the company smarter even if the first release is narrow.
Reusable LMS knowledge
Capture confirmed SCORM, xAPI and LTI failure patterns into the company memory for future LMS delivery.
Delivery intelligence
Update delivery dashboards with completion-reporting risk signals and support-ticket leading indicators.
Next bets
Choose whether to scale to xAPI/LTI, add automated remediation, or convert the feature into an Anitech reusable LMS assurance asset.
The Executive Narrative
This is not just a feature. It is a demonstration of Anitech's AI-native delivery model: weak signals become a bounded bet, a human-agent workcell proves the outcome, risk is controlled, and the learning becomes reusable IP.