Stronger Testing.
More Reliable Reporting.
Keep data checks aligned with a changing business. Apply an AI testing agent to generate and maintain tests, run approved checks, and investigate failures across pipelines, warehouses, data marts, and BI outputs. Engineers approve test changes and remediation.
Schemas, SQL, documentation, and approved history
Quality checks, regression results, and likely causes
Coverage Across the Data Lifecycle
The proposed service combines deterministic tests with anomaly detection. Approved checks run on schedule or with pipeline changes; unexpected results become evidence for investigation, not automatic proof of bad data.
Schema & Contract Tests
Check columns, data types, required fields, and agreed contracts. Detect incompatible input or output changes before they propagate to dependent models.
Record-Level Quality
Test nulls, uniqueness, valid values, completeness, and referential integrity. Identify failing records or partitions with appropriate access controls.
Freshness & Volume
Detect late updates, missing partitions, unexpected row counts, and ingestion gaps. Use reporting deadlines and seasonal patterns to interpret anomalies.
Layer Reconciliation
Reconcile bronze-to-silver and silver-to-gold counts, keys, and totals. Account for approved filtering, deduplication, rejected records, and aggregation.
Transformation Regression
Generate fixtures and compare outputs with approved expected results or a trusted baseline. Test joins, calculations, incremental updates, and edge cases after SQL changes.
Pipeline Failure Diagnosis
Correlate orchestration status, logs, recent changes, and failed tests. Rank likely causes such as source delays, schema mismatches, or transformation errors.
Data Drift & Distributions
Check shifts in ranges, category mix, missingness, and numeric distributions. Separate plausible business changes from defects through historical context and owner review.
Data Marts & BI Outputs
Test semantic definitions, dashboard queries, refresh status, filters, and selected measures. Prioritize incidents by affected reports, business use, and reporting deadlines.
Tests That Evolve With the Data
The agent drafts tests automatically from schemas, SQL, documentation, and approved historical data. It proposes updates when those inputs change; engineers verify business meaning and approve new or revised tests.
Derive the Checks
Infer candidate keys, relationships, edge cases, and ranges. Attach each proposed assertion to its source evidence and intended business rule.
Validate the Tests
Review expectations and thresholds. Check that tests catch seeded failures as well as valid cases before adding them to the approved suite.
Run & Maintain
Execute approved tests through CI/CD and scheduled workflows. Detect coverage gaps and propose versioned updates as the data platform changes.
Explain & Recommend
Summarize failures, identify affected records and consumers, and recommend remediation. An engineer approves fixes before deployment.
Historical data is context, not ground truth. The agent must not silently weaken a test or reset a baseline simply to make a failure pass.
Missing Orders.
A Traceable Cause.
A revenue mart shows fewer orders than expected. The agent checks whether the bronze feed arrived, compares silver records with the approved filtering rules, and traces the gold aggregation.
If records disappear after a product join, failed relationship checks and recent SQL changes point to a likely mapping issue. The incident identifies the affected revenue report and the records excluded from the calculation.
The recommendation includes a mapping correction, regression tests, and controlled reprocessing. The engineer verifies the cause before approving the fix.
Severity With a Clear Response
Define severity and response policies with the data owners. A failed check on an executive revenue report may need a different response from the same failure on an exploratory dataset.
Critical · Contain
A material reporting or integrity risk. Alert the owner and hold affected publication or quarantine records only under an explicitly approved policy.
High · Investigate
A deadline, model, or important consumer is at risk. Prioritize the incident and prepare a tested fix for engineer approval.
Advisory · Review
A lower-impact anomaly or emerging drift. Preserve evidence and review the pattern before changing a rule, threshold, or baseline.
Audit Logs & Recovery
Retain test definitions, code versions, thresholds, run results, severity decisions, approvals, and actions in a durable audit history. Restrict access to failed-row samples. Quarantine preserves records for review; rollback follows an approved plan with defined retention and replay limits. Neither action is an unrestricted default response.
Native Checks, Agent Coordination
The agent operates across the tools already in the stack. Test generation, cross-layer reconciliation, BI validation, and remediation workflows form the proposed Rudder implementation scope.
Databricks Monitoring
The reference describes learned freshness and completeness signals, data profiling, and lineage-based prioritization. It also lists roadmap capabilities. The service verifies available features in the client environment rather than assuming the article describes a complete testing agent.
Agentic Data Quality Monitoring ↗dbt Data Tests
Built-in assertions cover nulls, uniqueness, accepted values, and relationships. Custom SQL extends coverage, with failing-record inspection where configured. Regression fixtures, anomaly detection, BI checks, and durable audit history require additional implementation.
dbt Data Tests ↗Defined Coverage. Accountable Ownership.
Start with critical datasets and the reports that depend on them. Agree on business rules, approved history, test schedules, access limits, alert owners, and the actions the agent may take.
Implementation & Handover
Receive a version-controlled test suite, coverage map, execution workflows, incident evidence format, and approval policies. Include a runbook for failures and recovery.
Ongoing Test Maintenance
Review missed incidents, noisy alerts, execution cost, and changes to data or BI models. Expand coverage deliberately, with business-owner approval for changed expectations.
BI coverage depends on access to report definitions, semantic models, and refresh metadata. Gaps remain visible in the coverage map. No agent can validate reports or business rules it cannot inspect.
A Clearer Standard for Data Quality
Bring a critical report, recurring pipeline failure, or gap in test coverage. Define the checks and approval controls needed to support dependable reporting.
