Agentic AI That Works Inside Your Data Pipeline
deKorvai's Agentic AI doesn't sit in a separate chat window — it works where your data lives. It auto-detects issues, suggests transformations, and generates data quality rules from a plain-language prompt, with a Co-Pilot and smart decision engine built into the platform.
natural-language prompt → generated rules → human approval → audit-logged
Agentic AI in data management means AI that doesn't just answer questions — it takes actions within a workflow: detecting issues, proposing changes, and making routine decisions under human oversight. deKorvai builds this into the platform rather than bolting on a chatbot.
Today it auto-detects data issues, suggests transformations, generates data quality rules from natural language, and makes smart decisions across the pipeline — through a Co-Pilot and the Agent Master, with every action approvable and audit-logged.
A chatbot beside your data isn't the same as AI inside it
Most "AI for data" means a separate assistant you copy-paste into. The work that actually slows teams down — writing rules, mapping fields, spotting issues — still happens by hand, in a different tool.
Writing rules is expert, manual work
Defining data quality and transformation rules needs someone who knows both the business and the syntax — a bottleneck that queues behind every data engineer.
Issues are found by whoever happens to look
Without something continuously watching, anomalies and bad records surface only when a person stumbles on them — usually downstream.
Detached AI can't act
A chatbot in a separate window can suggest ideas, but it can't apply a rule, run a check, or touch the pipeline where the work needs to happen.
Unchecked automation isn't trusted
Enterprises can't hand data decisions to a black box. AI has to propose, show its work, and wait for approval — with an audit trail.
Agentic capabilities shipping now
Every capability below is live in the platform today.
Auto-detect data issues
AI agents scan data and surface duplicates, gaps, and anomalies automatically — without waiting for someone to run a check.
Suggest transformations
The AI proposes transformation and mapping steps as you build a pipeline, so you start from a suggestion instead of a blank rule.
Generate DQ rules from natural language
Describe a check in plain language and deKorvai writes the data quality rule, ready to preview and approve.
Smart decision engine
The AI makes routine decisions across the pipeline — routing, branching, and next-step choices — inside the workflow.
Co-Pilot
An AI Co-Pilot assists with data tasks conversationally, turning intent into rules, checks, and actions in the platform.
Human approval & audit
Agent actions are proposed for approval and recorded — so AI accelerates the work without removing human control.
Propose, approve, act — with a record of every step
Agentic doesn't mean unattended. deKorvai's agents work in a loop that keeps a human in control.
Understand
State intent in natural language, or let agents scan data and flag what needs attention.
Propose
The AI generates the rule, transformation, or fix and previews exactly what it will do.
Approve
A human reviews and approves; nothing changes data without sign-off.
Act & log
The approved action runs across connected systems and is versioned in the audit trail.
Co-Pilot and Agent Master
Co-Pilot
The conversational assistant built into deKorvai. Ask it to profile a source, write a data quality rule, or suggest a transformation, and it turns your intent into a previewed, approvable action inside the platform — not a copy-paste snippet in a separate window.
Agent Master
The component that runs deKorvai's agents across the pipeline — coordinating detection, suggestions, and smart decisions, and connecting to models through MCP (Model Context Protocol). It's how agentic behavior stays consistent across data quality, ETL, and masking.
What Agentic AI changes for data teams
Rules in minutes, not tickets
Natural-language rule generation lets analysts create checks themselves, instead of queuing behind a data engineer.
Issues surfaced earlier
Agents watch continuously and auto-detect problems, so they're caught before they reach reports or a target system.
Less repetitive pipeline work
Suggested transformations and smart decisions remove the manual, repetitive steps that slow every build.
Automation you can trust
Every agent action is proposed, approved, and logged — the control and audit trail enterprises require.
One AI across every capability
The same agentic layer works across data quality, ETL, and masking — not a different assistant per tool.
Skills go further
Business users express intent in plain language; the AI handles the syntax, widening who can do data work.
Where Agentic AI helps
Natural-language rule authoring
Analysts describe data quality checks in plain language and the Co-Pilot writes the rules for approval.
Continuous issue detection
Agents auto-detect duplicates, gaps, and anomalies across connected data, continuously.
Guided transformations
The AI suggests transformation and mapping steps while building ETL pipelines — including for programs like an SAP S/4HANA move.
Smart pipeline decisions
The decision engine handles routine routing and branching choices inside the workflow.
Approvable, audited automation
Every agent action is previewed, approved, and logged for governance and audit.
Business-user self-service
Non-engineers express intent conversationally and let the Co-Pilot handle the technical detail.
Where deKorvai's Agentic AI is heading
Planned capabilities — shared for direction, not yet shipping.
Automated remediation suggestions
Agents will propose specific fixes for detected issues, not just flag them — still with human approval.
Smart anomaly detection with ML
Machine-learning models to detect subtler anomalies beyond rule-based thresholds.
AI-assisted data mapping
The AI will propose source-to-target field mappings during migration and integration work.
From AI beside your data to AI inside it
Bolt-on AI assistant
- →Lives in a separate chat window
- →Suggests, but can't act on your data
- →Rules and mappings still written by hand
- →Issues found only when someone looks
- →No record of what the AI advised or did
With deKorvai's Agentic AI
- ✓Works inside the platform where data lives
- ✓Proposes and — once approved — acts
- ✓Generates rules and transformations from intent
- ✓Agents auto-detect issues continuously
- ✓Every action approved and audit-logged
The agentic layer inside deKorvai
Agentic AI runs through the Agent Master and MCP, connecting the platform to a language model while the DQ Engine, ETL Engine, and Scrambler do the data work — one platform, one audit trail.
Documented integrations
The agentic layer works across Data Quality, ETL, and Data Masking — connecting to systems through JDBC, RFC/API, HTTPS, and REST. Explore Data Quality, ETL, and Data Masking.
Agentic AI, answered
Part of one unified platform
Agentic AI works across all of deKorvai — explore the capabilities it powers.
See Agentic AI work inside a real pipeline
Book a demo and watch the Co-Pilot turn a plain-language request into a previewed, approvable data quality rule.
Book a DemoA working session on where AI fits your data work