Agentforce data quality: what it is and how to fix it
“Data quality” for Agentforce isn’t a reporting nicety — it’s the difference between an agent that helps and one that misleads. Six measurable dimensions decide which one you ship.
What “data quality” means for an AI agent
Traditional data quality asks “is my reporting reliable?” Agentforce raises the stakes: every record is a potential input to a customer-facing answer. A blank field, a stale value, or a hidden SSN doesn’t just skew a dashboard — it shapes what your AI says out loud. Quality becomes a safety property.
The six dimensions that matter
Completeness
Are the fields agents read populated? Empty Descriptions force generic replies.
Consistency
Do values conform to one standard? Four spellings of “United States” become four facts.
Validity
Do structured fields pass format rules? Malformed emails and IDs get quoted verbatim.
Timeliness
Is the record current? Stale data produces confident, outdated answers.
Uniqueness
How many duplicates is retrieval juggling? Duplicates split a customer’s history.
PII Exposure
Where is sensitive data in free text? PII in context can surface in a reply.
How to measure it inside Salesforce
AgentforceSense — powered by Data Quality Sense — measures all six dimensions on-platform using Batch Apex. You configure scans with a no-code Definition Builder: choose the objects and fields Agentforce will read, set thresholds, and activate. Each scan returns a score per dimension, drill-down to the affected records, and CSV export for cleanup. Nothing is exported to an outside service — your data stays in your org.
- › Score every object an agent will touch, in minutes
- › Detect 8 PII patterns (SSN, card, IBAN, email, phone, IP, DOB) in free text
- › Let the Mentor Panel rank what to fix first
- › Schedule recurring scans to keep quality from drifting
See your data quality score
Book a walkthrough on your own org and find out exactly where your data would let an agent down.