Salesforce data quality benchmarks for AI agents
Salesforce data quality benchmarks are the score thresholds an org should hit — per metric, on every object an AI agent reads — before going live with Agentforce: 85%+ completeness, 90%+ conformance and validity, and under 1% PII exposure. Here is each number, and the reasoning behind it.
By the AgentforceSense team · Last updated June 12, 2026
The benchmark table
Recommended launch targets for the six dimensions of Agentforce data quality, measured on the objects and fields your agents will actually read — not the org-wide average, which hides exactly the problems that surface in agent answers.
| Metric | Launch benchmark | What it measures |
|---|---|---|
| Completeness Rate | 85%+ | Share of key fields that are populated on agent-read objects. |
| Conformance Rate | 90%+ | Share of picklist and reference values that match one approved standard. |
| Validity Rate | 90%+ | Share of structured fields (emails, phones, IDs) that pass format rules. |
| Timeliness Rate | your window | Share of date-sensitive records updated within your defined freshness window. |
| Duplicate Rate | low, trending down | Share of records on agent-read objects that have a likely duplicate. |
| PII Exposure Rate | < 1% | Share of scanned records with a PII pattern match in free text. |
Why is the completeness benchmark 85% and not 100%?
Because chasing the last 15% has sharply diminishing returns. Some emptiness is legitimate — optional fields, records that predate a process, objects where a value genuinely doesn’t apply. At 85%+ on key fields, an agent almost always has enough context to answer specifically instead of generically. Below that, vague answers stop being edge cases and become the agent’s personality. Spend the remaining effort on consistency and PII instead — they fail louder.
Why do conformance and validity need 90%?
Inconsistent and invalid values don’t just weaken answers — they create contradictions. When “US”, “USA”, and “United States” coexist, an agent can give three different answers to the same question, and retrieval can miss records that are filtered by value. Invalid emails and IDs get quoted to customers verbatim. These failures are visible to users on the first bad record they hit, which is why the bar sits higher than completeness.
Why must PII exposure be below 1%?
Because PII is the only metric where a single record can be an incident. An SSN pasted into a Case comment can surface in an agent reply, and at that point the problem is regulatory, not cosmetic. Under 1% is a working ceiling for launch sign-off, not a finish line — the operational goal is zero findings on SSN and credit-card patterns, verified by a scan, with the rest trending to zero through weekly monitoring.
Why don’t timeliness and duplicates get a universal number?
Freshness is business-specific: a support org may need cases touched within days, while a manufacturer’s account data can be a quarter old and still correct. Set your own window per object, then hold it. Duplicates are similar — the acceptable rate depends on object volume and matching rules, so the benchmark is directional: low, measured, and trending down, because every duplicate splits a customer’s history across records the agent can’t reconcile.
How often should you re-measure?
Benchmarks decay — users keep entering data after go-live. The cadence that keeps scores at launch level:
PII Exposure
High-volume text fields collect pasted customer messages daily; PII risk regrows fastest.
Completeness & Consistency
New records and new users steadily erode both; monthly catches drift before agents do.
All six metrics
A full sweep across every agent-read object resets your baseline and catches slow decay.
For the full pre-launch sequence — what to measure and fix in the 90 days before go-live — use the Agentforce launch checklist.
Get your numbers in 15 minutes
AgentforceSense scores your org against every benchmark on this page — natively in Salesforce, with drill-down to the records that miss the bar.