Agentforce Data Readiness Checklist

Agentforce data readiness is the state in which your Salesforce records are complete, consistent, valid, fresh, deduplicated, and free of exposed PII, so that AI agents retrieve accurate context and generate trustworthy responses. Your data is ready for Agentforce when each of those six conditions is measured and meets a defined threshold, not assumed. This checklist gives you the items to verify, the reason each one matters, and the DQS metric that measures it.

Agentforce agents retrieve Salesforce records, generate responses grounded in CRM data, and take actions on behalf of users. Every one of those steps reads your data as the source of truth. If a field is blank, the agent has no context. If a value is spelled four ways, the agent treats it as four facts. If a Case comment holds a Social Security Number, the agent can surface it. Readiness means closing those gaps before deployment, not after an agent gives a wrong answer in production. For the full phased plan behind this list, see Agentforce Preparation.

How Do You Know If Your Salesforce Data Is Ready for Agentforce?

You know by measuring, not by inspecting records by hand. Run a DQS scan across every object the agent will access, then compare each metric against a target threshold. Work through the six areas below. Each checkbox is something you can verify with a scan result, so the answer to “is my data ready” becomes a number instead of an opinion.

Copy the checklist into your project notes and check items as you confirm them.

Scope and Access

Define what the agent touches before you measure anything. An agent that reads the wrong objects fails no matter how clean the rest of your org is.

• List every object Agentforce will read or write. The agent only retrieves what its topics and actions allow. Scope your scans to that exact set so you measure the data the agent actually sees.
• List the fields within each object the agent uses for responses. Description, Comments, Notes, and key picklists carry the context. Scanning all fields wastes effort; scan the ones that ground answers.
• Confirm field-level security matches intent. An agent inherits the running user’s access. A field hidden from the user is invisible to the agent, so a “missing” answer is sometimes a permission gap, not a data gap.
• Record which integrations write to those fields. Integrations are the most common source of inconsistent and duplicated values. Knowing the writers tells you where to fix the intake, not just the data.

Completeness

Agents generate vague responses when the fields they read are empty. Completeness is the first thing to measure because missing context is the most common cause of weak agent output.

• Completeness Rate is 85% or higher on every field the agent uses for responses. A blank Description gives the agent nothing to ground an answer on. The Completeness Rate reports the percentage of records where the field holds a value.
• Critical context fields have no systemic blanks from a single source. A field left empty by one integration is a process fix, not a record-by-record cleanup. Field-level Completeness Rate isolates the source.
• Required-for-the-business fields are populated, even when not required in the schema. Agentforce treats a blank field as absence of fact. See Completeness for how DQS scopes “should be filled” per object.

Consistency and Validity

When the same real-world value appears in several forms, the agent treats each as distinct and answers inconsistently. When a value breaks its expected format, downstream retrieval becomes unreliable.

• Conformance Rate is 90% or higher on picklist and reference fields. A Country field holding “US”, “USA”, and “United States” splits one fact into three. The Conformance Rate reports the percentage of values matching your canonical set. Use Import from Field to discover existing variants, then define the canonical values. See Consistency.
• Validity Rate is 90% or higher on structured fields. Emails without an @, phone numbers with letters, and impossible dates produce unreliable retrieval. The Validity Rate reports the percentage of values passing your format rules. See Validity.
• Cross-field contradictions are resolved. A Billing State that contradicts its Billing Country teaches the agent a false relationship. Consistency checks surface values that disagree with each other.

Freshness and Duplicates

Stale data grounds answers in facts that are no longer true. Duplicate records give the agent two versions of one entity, so the response depends on which copy it retrieves.

• Timeliness Rate meets your target on date-sensitive fields. A Last Activity from two years ago or a Close Date in the past misleads the agent. The Timeliness Rate reports the percentage of records current within your defined window. See Timeliness.
• Duplicate Rate is low on the objects the agent reads. When the agent retrieves one of three records for the same customer, it answers from partial history. The Duplicate Rate reports the percentage of records that duplicate another. See Uniqueness.
• Merge or flag duplicates before deployment, not after. Duplicates created across forms, imports, and manual entry multiply without monitoring. Resolve them so the agent reads one record per entity.

PII and Compliance

PII in the text fields an agent reads enters the AI context and can appear in a generated response. This is the area where a readiness gap becomes a compliance incident.

• PII Exposure Rate is below 1% on text fields the agent accesses. SSNs and credit-card numbers accumulate in Description, Comments, and Notes through copy-paste and email-to-case. The PII Exposure Rate reports the percentage of records containing a pattern match. See PII Detection.
• Zero SSN or credit-card matches on Case Description and Comments. Financial PII is the highest-severity finding. Run the Critical preset scan to isolate it, then mask, delete, or exclude confirmed matches.
• Per-field pattern overrides are configured for expected-content fields. An Email field matches the email pattern by design, which is noise, not exposure. Per-field overrides remove that noise so the rate reflects real risk.
• Compliance team has signed off on the post-remediation PII scan. GDPR, CCPA, HIPAA, and PCI DSS require identifying and protecting PII. DQS runs entirely inside Salesforce, so no records leave the org during detection. See Agentforce PII Compliance.

Operations and Monitoring

Data quality degrades as users enter new records. A dataset that passes today accumulates new issues within weeks, so readiness is a state you maintain, not a milestone you pass once.

• Baseline metrics are documented for every dimension in scope. You need the starting numbers to prove remediation worked and to detect regression later.
• A recurring scan schedule is configured. Scheduled scans turn a one-time snapshot into a trend line. Run PII weekly on high-volume text objects, completeness and consistency monthly, and a full scan quarterly.
• Remediation ownership is assigned per dimension. A metric with no owner does not improve. Name the person responsible for completeness, for PII, and so on.
• Agent responses are tested against remediated data. A passing scan predicts good behavior; a test confirms it. Verify outputs are accurate and that no PII appears in generated content.

Readiness Thresholds at a Glance

Use this table as the pass criteria for each area. The targets match the Agentforce Preparation plan.

What If My Org Fails Several Items?

A failing checklist is the expected starting point, not a reason to delay. Most orgs discover issues they did not expect on the first scan. Prioritize in this order: PII first, because it carries compliance risk; then completeness and consistency, because they shape the quality of agent context; then validity, timeliness, and uniqueness. Fix the highest-impact, lowest-effort items first, rerun the scan, and compare against your baseline. For a step-by-step remediation path, see Salesforce Data Cleanup for Agentforce and Why Agentforce Agents Fail.

Next Steps

• Agentforce Preparation: the full phased readiness plan behind this checklist
• Salesforce Data Cleanup for Agentforce: how to remediate failing items
• PII Detection: configure PII scanning for the fields agents read
• Why Agentforce Agents Fail: the data problems behind weak agent output
• Agentforce Data Quality FAQ: quick answers to common readiness questions
• How to Measure Data Quality in Salesforce: the Data Quality Score in depth
• AI Readiness Assessment: get your current readiness score