Salesforce Data Cleanup for Agentforce: Object-by-Object Guide

Which Salesforce objects you clean first depends on which agents you deploy. Service agents read Case and Knowledge; sales agents read Lead, Opportunity, Account, and Contact. This guide covers the where of Salesforce data cleanup for Agentforce — the specific objects and fields to clean, the dimension at risk on each, and the cleanup actions to run.

For the when — the four-phase timeline that surrounds this work — see Agentforce Data Quality: Preparing Salesforce Data for AI. This article fits inside Phase 2 of that timeline, where you remediate object by object. Run a DQS scan on each object before you start so you know which fields actually fail, rather than cleaning fields that are already fine.

Cleanup at this level is not optional groundwork. In Salesforce’s State of Data and Analytics research, 84% of data and analytics leaders said their data strategy needs a complete overhaul before their AI ambitions can succeed. Object-by-object remediation is what that overhaul looks like in practice inside a CRM.

Account

Sales agents read Account to ground responses about a customer’s industry, location, and relationship. When the agent answers “what do we know about this account,” it draws from these fields.

Cleanup actions:

• Run a Uniqueness scan to find duplicate Accounts before deployment. Duplicates teach the agent contradictory facts about the same customer. See Uniqueness.
• Use Import from Field on Industry and BillingCountry to discover every variant that exists, then define canonical values in the Definition Builder and normalize.
• Run a Validity scan on Phone and Website to catch malformed entries. Target fields where Validity Rate falls below 90%.

Contact

Agents read Contact to identify who they are dealing with and how to reach them. A sales agent drafting an outreach message pulls the name, title, and email from here.

Cleanup actions:

• Run a Validity scan on Email and Phone. These are the fields an agent acts on, so format errors turn into failed actions. See Validity.
• Run a Completeness scan on Title and measure Completeness Rate. Missing titles strip the agent of the role context it needs to tailor a message.
• Standardize MailingCountry with Import from Field, matching the canonical list you defined on Account so the two objects agree.

Case

Service agents work primarily from Case. The agent reads the Subject and Description to understand the issue, then grounds its reply in that context. This is the highest-leverage object for a service deployment.

Cleanup actions:

• Run a Completeness scan on Description. Description completeness equals agent context; a blank field is the most common reason a service agent answers vaguely. See Completeness.
• Standardize Status and Origin using Import from Field to surface every existing value, then normalize to your canonical picklist.
• Run a PII scan on Description and Case Comments. Email-to-case captures customer messages that contain PII, and the agent can surface that PII in a response. See PII Detection and the Agentforce PII compliance guide.

Lead

Sales agents read Lead to qualify and route inbound interest. The agent decides next actions from the company, source, and status, so gaps here send leads down the wrong path.

Cleanup actions:

• Run a Validity scan on Email and a Completeness scan on Company. These two fields drive whether the agent can act on a lead at all.
• Use Import from Field on LeadSource and Status to find drift, then constrain to a defined value set. See Consistency.
• Run a PII scan on any notes or description fields where reps paste raw inbound messages.

Opportunity

Sales agents read Opportunity to answer pipeline and forecast questions. A stale stage or missing amount produces a confidently wrong answer about deal status.

Cleanup actions:

• Run a Timeliness scan to flag open Opportunities with CloseDate in the past. Stale dates make the agent report a pipeline that no longer exists. See Timeliness.
• Run a Completeness scan on Amount. An agent summarizing pipeline value cannot do so reliably when amounts are blank.
• Confirm StageName matches your defined sales process with a Consistency scan.

Knowledge

Service agents ground answers in Knowledge articles. The agent retrieves an article and presents its content as the authoritative answer, so a stale or thin article becomes a wrong answer delivered with confidence.

Cleanup actions:

• Run a Timeliness scan against the last modified date to surface articles past your freshness threshold. Outdated articles are the leading source of confidently wrong service answers.
• Run a Completeness scan on Title and Summary. The agent uses these for retrieval, so weak metadata leads it to the wrong source.
• Run a PII scan on the article body to confirm no customer-specific data was pasted into a published article.

Which objects should you clean first?

Clean the objects your agents actually read, in the order their answers depend on them. The matrix below maps cleanup priority to agent type.

For a service deployment, start with Case Description completeness and Knowledge timeliness, because those two fields determine whether the agent has correct context to answer from. For a sales deployment, start with Account and Contact, then move to Lead and Opportunity. Across every agent type, run PII scans first on the text fields the agent reads, since a PII finding is a compliance issue rather than a quality one. The Agentforce data readiness checklist lists the pre-deployment thresholds to hit on each object.

Next Steps

• Agentforce Data Quality: Preparing Salesforce Data for AI: the four-phase timeline this cleanup fits inside
• Why Agentforce Agents Fail: the data problems behind unreliable agent output
• Agentforce Data Readiness Checklist: pre-deployment thresholds per object
• How to Improve Data Quality in Salesforce: the detect, fix, prevent, monitor loop
• Agentforce Data Quality FAQ: common questions answered
• AI Readiness Assessment: get your current readiness score