AI Agent for Testing
Stronger Testing.
More Reliable Reporting.
Keep data tests aligned as pipelines and business rules change. Rudder Analytics builds AI agents to draft tests, run approved checks and investigate failures. Engineers approve new or revised tests and production fixes.
Schemas, SQL, business rules and reference data
Approved checks, test results and likely causes
Coverage Across the Data Lifecycle
Find data problems before report readers do. Rule-based tests and anomaly detection cover pipelines, warehouses, data marts and BI outputs. Approved checks run on schedule or with pipeline changes.
Schema & Contract Tests
Flags an incompatible change for review before the models built on it run.
Columns · data types · required fields · agreed contracts
Record-Level Quality
Pinpoints failing records or partitions, under appropriate access controls.
Nulls · uniqueness · valid values · completeness · referential integrity
Freshness & Volume
Spots late or missing data, judged against reporting deadlines and seasonal patterns.
Late updates · missing partitions · row counts · ingestion gaps
Layer Reconciliation
Reconciles counts, keys and totals across layers, accounting for approved filters, deduplication and aggregation.
Bronze to silver · silver to gold · approved filtering and deduplication
Transformation Regression
Shows whether a SQL change altered results, against approved expectations or a trusted baseline.
Joins · calculations · incremental updates · edge cases
Pipeline Failure Diagnosis
Ranks the likely causes of a failure, such as source delays or schema mismatches.
Orchestration status · logs · recent changes · failed tests
Data Drift & Distributions
Flags distribution changes and uses historical patterns and owner review to assess whether they reflect business shifts or data defects.
Ranges · category mix · missingness · numeric distributions
Data Marts & BI Outputs
Checks dashboards against approved business definitions and ranks incidents by reporting deadline.
Accessible semantic models · dashboard queries · refresh status · filters · measures
Tests That Evolve With the Data
The agent uses schemas, SQL, documentation and reference data to propose tests and identify gaps as the platform changes. Engineers verify the expected results and business rules before adding checks to the approved suite.
- 01
Derive the Checks
Infer candidate keys, relationships, edge cases and ranges. Attach each proposed assertion to its source evidence and intended business rule.
- 02
Validate the Tests
Review expectations and thresholds. Confirm that tests detect seeded failures and pass valid cases before adding them to the approved suite.
- 03
Run & Maintain
Execute approved tests through CI/CD and scheduled workflows. Detect coverage gaps and propose versioned updates as the data platform changes.
- 04
Explain & Recommend
Summarize failures, identify affected records and consumers, and recommend remediation. An engineer approves fixes before deployment.
Historical patterns inform proposed tests. Changes to rules and baselines require review, including when an existing test fails.
Missing Orders.
A Traceable Cause.
A revenue mart shows fewer orders than expected. The agent checks the bronze feed, the silver filters and the gold aggregation.
Records drop after an inner join to product data. Failed relationship checks and recent SQL changes may point to unmatched product keys.
The agent proposes a fix for the unmatched keys, regression tests and controlled reprocessing. The engineer verifies the cause and the proposed correction before approving release.
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 under an approved response 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, results, approvals and actions in an audit history. Limit access to failed-row samples. Quarantine and recovery follow agreed procedures, with retention and replay limits documented in the runbook.
Native Checks, Agent Coordination
Rudder Analytics connects existing testing and monitoring tools with agent-led investigation. The implementation brings together test generation, reconciliation, BI checks and reviewed remediation.
Databricks Monitoring
Use freshness and completeness signals alongside table profiles to investigate unexpected changes. Connect available lineage and run history to assess downstream impact and prioritize incidents.
Databricks Data Quality Monitoring ↗dbt Tests
Use dbt data tests for nulls, uniqueness, accepted values and relationships. Add unit tests for SQL logic and custom checks for business rules. Connect results to the agent’s investigation and review workflow.
dbt Testing Documentation ↗Defined Coverage. Accountable Ownership.
Start with critical datasets and the reports that depend on them. Agree on business rules, reference data, 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, with 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.
The coverage map identifies which reports, semantic models and refresh processes can be tested, along with access or definition gaps to resolve.
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.
