If you’ve been following Salesforce lately, you’ve probably heard about Agentforce—the new AI platform that lets businesses build and deploy their own digital agents. It’s exciting stuff. But here’s the reality: no matter how advanced the AI is, it’s only as good as the data it runs on.

We’ve seen companies rush to plug in new AI tools, only to find out they can’t actually trust what comes out the other side. The culprit is almost always the same—messy, inconsistent data.

So before you jump into building your first Agentforce agent, take a beat. Let’s talk about how to get your data ready so the tool actually works the way it’s supposed to.

Why Getting Your Data Right Comes First

Agentforce connects directly to your Salesforce data and other business systems to perform actions automatically. As Salesforce puts it, Agentforce is designed to “combine data, reasoning, and action” to get work done.

That sounds powerful—and it is—but it also means that whatever data your system holds, that’s what your AI will act on. If you’ve got duplicates, missing fields, or outdated records, your agents could end up making bad calls or triggering the wrong workflows.

Before you give AI the keys, make sure your house is in order.

1. Know Where Your Data Lives

Start by figuring out where your data actually lives and how it flows. For most teams, this means Salesforce at the center, but other systems—like ERP, marketing automation, or customer success platforms—are probably in the mix too.

Spend some time mapping out:

• Which data sources Agentforce will touch.
• Which fields matter most for decision-making.
• How data moves between systems (and where it breaks).

It sounds tedious, but it’s worth it. Salesforce’s Data Management Best Practices Guide is a great reference if you need a framework.

Start with a whiteboard session or spreadsheet that lists every data source, the owner, and how often it updates. It’s not fancy, but it’s honest—and honesty about your data is step one.

2. Clean It Like You Mean It

Once you know what you’ve got, it’s time for cleanup. This is where a lot of companies struggle, but it’s the most important part.

Here’s what to focus on:

• Duplicates: Merge them. They’ll confuse your agents and your reports.
• Missing key fields: If “Account Owner” or “Renewal Date” are blank, fix them now.
• Standardization: Make sure your naming conventions, formats, and field values are consistent.
• Old junk: Archive or delete stale records you haven’t touched in years.

Agentforce isn’t magic; it can’t “figure it out.” If the data is wrong, it’ll just act on that wrong data faster than a human could.

We usually recommend running a data health check before you even open the Agentforce setup screen. A few hours of cleanup now can save you months of debugging later.

3. Structure Your Data for Action

Agentforce doesn’t just look at data; it acts on it. That means your Salesforce data model has to make sense logically.

If your objects and relationships are unclear, your agent won’t know how to navigate them. For example:

• A “Service Case” should tie to the right “Account.”
• A “Contract” should link to “Opportunities” or “Subscriptions.”
• Custom objects should have clear parent–child relationships.

You don’t need to overcomplicate it—just make sure every piece of information connects the way your business actually works.

Salesforce’s Agentforce overview shows how agents reason through connected data, so clean relationships are key. The tighter your structure, the smarter your AI will behave.

4. Build Guardrails and Ownership

Once your data is structured, put a governance plan in place. This is where Revenue Ops really earns its keep.

Ask yourself:

• Who owns the data for each object?
• Who reviews changes or additions?
• How often are audits done?

Set clear ownership early. Agents act quickly, so you want to know who’s responsible for fixing or approving their behavior if something goes off track.

Salesforce’s Trust Layer framework—which you can read more about in their Einstein GPT announcement—is worth reviewing for inspiration. It emphasizes data privacy, oversight, and control—all things that apply here too.

5. Start Small, Then Scale

It’s tempting to build five agents right away, but don’t. Start with one small use case—something measurable and low-risk, like:

• Logging meeting notes.
• Updating case statuses.
• Flagging missing data fields.

Once you’ve proven the agent works and your data supports it, expand gradually. Each new use case will probably surface a few more data issues—and that’s okay.

Treat early Agentforce projects as pilots. Measure what worked, document what didn’t, clean up what broke, and only then roll it out wider. It’s a “learn fast” mindset, not “launch fast.”

The Real Secret to Agentforce Success

You can’t build smart automation on messy data. It’s that simple.

Agentforce is a powerful tool—but like every Salesforce innovation before it, it’s built on one core truth: your CRM is only as strong as its data.

Before you start designing clever agents or complex workflows, make sure the foundation is rock solid. Get your data clean, organized, and well-governed. Then, and only then, let Agentforce do what it does best—work smarter and faster than any human could alone.

If you’re not sure where to start or want someone to review your data model before you roll out Agentforce, we can help. Check out Our Services—this is exactly what we do every day.