Agentic Data Quality Assistant

Problem

Data-quality incidents often spread through reporting and automation before the right owner has enough context to act.

An agentic assistant can create leverage only if it is designed around evidence, confidence, policy boundaries, approval, and auditability rather than autonomous action.

Users and stakeholders

• Data product owners responsible for reliability and business definitions.
• Analytics and operations teams affected by freshness, schema, volume, or quality changes.
• Platform engineers who need triage context before remediation.
• Governance and business stakeholders who need to know when data is safe for decision workflows.

Product surface

• Monitoring panel with fictional pipeline signals and severity.
• Incident detail panel with evidence, confidence, likely cause, and recommended action.
• Human decision controls for approve, reject, escalate, and reset.
• Audit log showing decision state, policy boundary, confidence, and follow-up context.

System shape

Agentic data-quality workflow

1. Pipeline signal
2. Incident detection
3. Evidence-backed explanation
4. Human decision
5. Audit and follow-up metric

Governance and risk controls

• The assistant can recommend remediation, but production action requires accountable human approval.
• Every recommendation must show supporting evidence, confidence, and a policy boundary.
• Low-confidence or sensitive incidents should escalate to the owner role instead of being approved inline.
• The audit log should capture why a decision was made, not only that a button was clicked.
• Follow-up metrics should indicate whether the incident was resolved, repeated, or promoted into roadmap work.

Metrics

• Triage cycle time Time from incident detection to accountable owner decision.
• Evidence coverage Percent of recommendations with freshness, schema, volume, or quality evidence attached.
• Approval quality Reviewer agreement rate and rollback rate for approved remediation paths.
• Escalation accuracy Percent of sensitive or low-confidence incidents routed to the correct owner role.
• Repeat incident rate Frequency of recurring quality incidents after remediation or owner review.
• Audit completeness Percent of decisions with confidence, evidence, policy boundary, owner role, and follow-up state.

Prototype assumptions

• All incidents are fictional and deterministic.
• The assistant does not call an external model or API.
• Recommendations are product examples, not operational automation.
• The UI is intentionally focused on incident workflow, not chat.

Evaluation strategy

• Use reviewed incident examples as a golden set before allowing higher automation.
• Track recommendation correctness, missing evidence, unsafe suggestions, and reviewer disagreement.
• Measure whether teams resolve incidents faster without reducing accountability.
• Promote repeated incidents into roadmap work when manual approval is no longer the right bottleneck.

Interactive prototype

Mock incident workflow with evidence, confidence, policy boundary, human decision controls, and audit log. The demo uses static fictional data only.

Scaling-company takeaway

A lean data team can use AI to improve incident response without pretending the system is fully autonomous. The useful product pattern is recommendation plus evidence plus accountable approval.