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Agent | Data Reconciliation

Stop digging for answers when reconciliation breaks.

The Reconciliation Agent queries your source and destination data to surface what's failing, why, and what your team should act on — through conversations, & not IT tickets.

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TRUSTED BY ENTERPRISE DATA TEAMS WORLDWIDE

Agent’s Core Power. Unleashed.

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

Query & Surface Failures

Diagnose Root Causes

Escalate & Report

You stay in control. Always.

Review before you escalate

Validate the agent's failure analysis. Your call on whether it goes to the team or gets filed as a ticket.

Correct a misclassification

Override the root cause category and feed that correction back — the agent learns from your domain knowledge.

Approve the ServiceNow ticket

Confirm priority and supporting data before the ticket is filed. No surprises for the on-call team.

Sign off on trend reports

Review 90-day pattern summaries before they reach finance or compliance. Your name, your confidence.

Scope the investigation

Set which systems, time windows, and migration phases the agent queries. Nothing runs outside your guardrails.

Ask the Reconciliation Agent Anything

Migration Validation

“Show me the current data quality scores for our customer tables.”

Which datasets have the lowest completeness this week?

Root Cause Investigation

"Is the Kafka→BigQuery failure a pipeline issue or bad data in the source?"

When did the quality score start declining for the product catalog?

Escalation & Reporting

"Open a P1 ServiceNow ticket for the Kafka recon failure with the last 7 days of supporting data."

Generate a weekly quality report for executive review

Migration doesn't end at cutover.

The hardest part is validating that every row, every pipeline, every layer landed correctly. That's what this agent is built for.

Before

45 min/incident correlating logs (Kafka, Snowflake, BigQuery) manually.

Dataplane outages misread as data quality issues; engineers chase wrong fixes.

Recon results and incident tickets separate; recurring failures invisible until escalated.

After

Ask once, get classified failures and root cause in < 30 seconds.

Six failure types auto-classified; infra vs. data mismatch always distinguished.

Recon data and incident history queried together; patterns surface

Reconciliation Agent in Action

Powered by the xLake Reasoning Engine, the Reconciliation Agent works across your entire data stack — from legacy source to migrated target.

Ask. Get answers across every layer.

One natural language question queries recon policy results and incident records across your connected platforms — Oracle, Kafka, Snowflake, BigQuery, Databricks — simultaneously.

Know the failure type before you investigate.

Spark execution crash, dataplane outage, policy logic mismatch, empty dataset — the agent classifies it automatically. No more spending 45 minutes ruling out the wrong cause.

Surface the pattern hiding in plain sight.

Correlate recon failures with open incident tickets. Recurring failures with no active resolution — the ones that slip through every migration review — become visible.

HITL:

Validate Root Cause Classification

Escalate without switching tools.

Confirm the pattern, then use the ServiceNow MCP connection to open a high-priority ticket with full supporting data attached — in the same conversation.

Generate the report your stakeholders need.

Produce audit-ready discrepancy reports with lineage drill-down for migration sign-off, compliance reviews, and finance close cycles — on demand, not on request.

HITL:

Approve Ticket Creation & Priority

Got Questions? Get Clarity

Q1. Does it work during an active cloud migration, not just after?

Yes. The agent queries recon policy results and incident data across your legacy source and migrated target simultaneously — so you can validate consistency at every phase, not just at final cutover. It's built for teams running parallel environments across Oracle, Kafka, Snowflake, Databricks, and BigQuery.

Q2. Does it fix mismatches automatically?

No — the Reconciliation Agent surfaces what's failing and why. It classifies root causes, identifies patterns, and helps your team know exactly what to act on. Where MCP connections exist (like ServiceNow), it assists with escalation — but every action requires your approval.

Q3. How does the ServiceNow integration work?

Via an MCP-DC server connection. Query recon failures and incident history together, confirm the pattern, then open a prioritized ServiceNow ticket with supporting data attached — in the same conversation. No copy-paste, no context switching.

Q4. How is this different from querying my recon tool directly?

Your recon tool shows policy results in isolation. This agent queries recon data and incident records together, classifies failures by root cause type, and correlates patterns across your entire stack — including schema drift, pipeline history, and lineage. That cross-system context is what separates a 30-second answer from a 45-minute investigation.

Q5. What failure types does it classify?

Six: Spark execution failures, infrastructure/dataplane issues, policy logic errors (where the job succeeds but the policy shows ERRORED), empty dataset errors, execution timeouts, and genuine data mismatches. For migration teams, knowing whether you're looking at a dataplane outage or a real row-count divergence is the difference between a quick infra fix and a data remediation project.

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