Agents | Data Quality

Data Quality. Powered by Autonomy.

The Data Quality Agent finds, fixes, and prevents issues-autonomously-so your pipelines stay trusted and AI-ready, without manual firefighting.

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Agent’s Core Power. Unleashed.

Where every decision is powered by reliable, self-healing data.

Detect Issues Proactively
Diagnose with Context
Resolve & Prevent Automatically

You + AI,
Working Together

Review anomalies
‍before actions
Validate findings before automated remediation kicks in.
Override false positives
Dismiss or reclassify events and retrain detection logic.
Approve policy enforcement
Apply or pause rules dynamically based on operational context.
Validate RCA recommendations
Confirm or correct root cause suggestions surfaced by the agent.
Prevent Future Failures
Recommend adaptive guardrails and freshness policies based on historical patterns.

Data Quality Agent in Action

Powered by the xLake Reasoning Engine, the Data Quality Agent operates as part of a collaborative, agentic framework:

Monitor Continuously
Scan batch jobs, pipelines, and tables for quality violations.
Analyze with Lineage
Identify source of drift, duplicates, missing values, and stale records.
Diagnose Root Causes
Connect symptoms to pipeline logic, upstream schema changes, or unexpected inputs.
Remediate Proactively
Auto-reprocess only impacted data, flag unresolved records, and document findings.
HITL:
Validate Anomalies
Prevent Future Failures
Recommend adaptive guardrails and freshness policies based on historical patterns.
HITL:
Approve Remediation

Ask the Data Quality Agent Anything

Information Retrieval
“Show me the current data quality scores for our customer tables.”
Which datasets have the lowest completeness this week?
RCA / Investigation
What’s causing the drop in accuracy for our transaction data?
When did the quality score start declining for the product catalog?
Action / Resolution
“Trigger remediation for tables with low accuracy”
Generate a weekly quality report for executive review
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Always-On Data Quality, Always AI-Ready

Where every decision is powered by reliable, self-healing data.

From
Manual rules and thresholds
Reactive fixes, delayed resolution
Static and limited coverage
To
AI learns and adapts quality expectations
Root cause analysis + auto-remediation
Context-aware validation across lineage

Enterprise Outcomes, Realized

“ADM’s planning reduced data downtime by 50% and quality incidents by 80%. Our team now focuses on strategy, not reactive cleanup.”

- Data Lead, Fortune 500 Financial Enterprise

Got Questions? Get Clarity

Q1. What makes this different from other data quality tools?

The Data Quality Agent is autonomous, contextual, and embedded in aDM. It doesn’t just detect issues—it understands them, acts on them, and gets smarter over time.

Q2. Can it fix data issues automatically?

The Data Quality Agent is autonomous, contextual, and embedded in aDM. It doesn’t just detect issues—it understands them, acts on them, and gets smarter over time.

Q3. How does it work with lineage?

The Data Quality Agent is autonomous, contextual, and embedded in aDM. It doesn’t just detect issues—it understands them, acts on them, and gets smarter over time.

Q4: How customizable are the data quality rules?

The Data Quality Agent is autonomous, contextual, and embedded in aDM. It doesn’t just detect issues—it understands them, acts on them, and gets smarter over time.

Q5: What kinds of data issues can it detect?

The Data Quality Agent is autonomous, contextual, and embedded in aDM. It doesn’t just detect issues—it understands them, acts on them, and gets smarter over time.

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