
IDMC

Agentforce






























The data work still gets done by hand: someone profiles the tables, writes the quality rules, traces the lineage, finds the sensitive columns, and keeps the catalog current. Nine of our agents already do that work in production, inside your own environment, with a steward approving every change.
Runs the day-to-day work of your catalog whenever you ask in plain language.
23 tools in production, catalog search through glossary publish
See the technical layerData Governance · Function: Governance Agent
Locate the asset and its connection before anything is touched.
Configured against the live connection, not a copy.
Statistics computed directly from source where the platform supports it.
Completeness, validity, consistency and uniqueness rules tailored to what the profile found.
Rule definitions written natively into your data quality tool.
The job that runs the rules at scale.
One command takes a new dataset from unknown to governed.
7-step orchestration live, with per-step status and timing
See the technical layerData Governance · Function: Data Onboarding
Check whether the asset is already known.
Compute the statistics everything downstream depends on.
Identify sensitivity before the data is exposed to anyone.
Recommend and author rules from the profiling evidence.
Register the asset, its scores and its rule results.
Create or link the business terms that make it findable.
Optionally publish as a data product, ready to be ordered.
Profiles any table, drafts the metadata, writes the rules, and proves each one.
45 rules executing · 1,622 records scanned · 3–5× throughput at ~$0.03/rule
See the technical layerData Quality · Function: DQ Agent
Every column profiled and flagged with evidence: nulls, distributions, patterns, date ranges, duplicates.
Business descriptions and domain terms drafted automatically, at table and column level.
DQ rules with ready-to-run SQL and the evidence behind each.
A steward reviews, edits the SQL, and approves or rejects.
Approved rules run on schedule.
Watches quality scores over time and tells you what is drifting, and what to do.
4 tools in production, trend detection against score history
See the technical layerData Quality · Function: DQ Monitor
Current quality scores for any registered asset.
Detect degradation and drift relative to previous runs.
Turn raw failures into a structured picture of what broke and where.
A specific next action, not just a red number.
Register alerting so coverage does not depend on somebody remembering.
Writes the descriptions nobody ever writes, for every table and every column.
AI-written, steward-approved, no authoring backlog
See the technical layerMetadata Management · Function: AI business & technical descriptions
Column names, types, sample values, table context and neighboring columns.
Technical and business descriptions at both table and column level.
Approve, edit or reject.
Approved descriptions land in the catalog and the glossary.
Builds and defends the business glossary: terms, domains, and the collisions between them.
5 tools in production, scanning the live glossary for collisions
See the technical layerMetadata Management · Function: Glossary Manager
Proposed from a technical asset’s columns and context.
Business terms added to your catalog with their definitions.
Domain, subdomain and term, created and linked as one operation.
The live glossary scanned for duplicates, orphans and definition gaps.
Traces where data came from and what breaks if you change it.
3 tools in production, tracing into Snowflake, Databricks and Microsoft Fabric
See the technical layerData Architecture & Integration · Function: Lineage Reporter
Walk back through every transformation to the systems of record.
Find every consumer, report and copy that depends on the asset.
Impact reports ordered by consequence, not alphabetically.
Identify the true origin behind a derived asset.
Converts integration estates to a new stack, with every manual review item flagged.
11 component mappings covered, 4 generated artifact types per path
See the technical layerData Architecture & Integration · Function: AI Migration Services
Mappings, tasks, chained jobs, processes and connections read out of the source platform.
Each component mapped to its target equivalent: flows, transformations, scheduled workflows, SQL models, workflow steps.
Build-ready output: project structure, dependencies, configuration, deployment descriptors.
A coverage report names what converted cleanly and what needs manual review.
Finds sensitive data and weighs four layers of evidence before anything is tagged.
40+ PII patterns · dual registration to your platform and governance catalogs · audit trail per tag
See the technical layerData Privacy & Compliance · Function: AI Data Classification
Catalog API enumerates the estate.
40+ PII patterns: Social Security number, credit card number with checksum validation, email, phone, postal code, IP address, international bank account number.
Column name, 20 sample values, table context and neighboring columns go to the model.
Layers 1 and 2 agreeing auto-tags.

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