Make your Salesforce automations and AI agents reliable

The problem isn't your automation. It's what's underneath it.

You build a workflow. You configure an agent. You test it, deploy it, and move on. Then, weeks later, something breaks downstream: a campaign skips half its audience, an agent answers with a blank, a routing rule fires on the wrong record.

You dig in. A field that was 90% populated when you built the automation is sitting at 55% now. Nobody changed the automation. The data underneath it changed.

This is how most Salesforce automation failures happen. Not from bad logic, but from fields that were never checked before go-live, or that drifted after it.

Think of it like a full-body MRI for your Salesforce data. A single scan gives you an in-depth view of what’s there at that point in time, and just like with your health, one scan isn’t enough. You need to keep scanning. Without ongoing monitoring, a field can go from healthy to critically degraded, and nothing flags it. Not Salesforce. Not your automation. Not your AI agent. You find out when something breaks.

In practice, that looks like this: 30% of lookup fields referencing deleted parent records. 15% of date fields containing impossible future values. A contact email sitting at 63% fill rate, feeding an AI model nobody told the data was incomplete.

The biggest AI risk isn't the bad data you see. It's the bad data you don't.

Two signals. Two scenarios. One pattern.

These are the two data problems that silently break automations and mislead AI agents most often.

Fill rate

An enrollment workflow assumes the contact email field is populated. It isn't. Not for 40% of records. The workflow skips them. Nobody notices for three weeks, until the campaign team asks why open rates are down.

An Agentforce agent uses the same field to personalize outreach. For those 40%, it either fails or defaults to something generic. The agent looks unreliable. The admin gets the call.

Plauti Context shows you the real fill rate for every field, so you know which ones are complete enough to build on before you build on them.

Duplicate value rate

The same account exists three times in your org. Your automation acts on the most recently updated record, which isn't the most complete one. Your AI agent pulls conflicting information from two versions of the same contact and surfaces the wrong history.

Before you can fix that, you need to know which fields are actually reliable enough to match on. Plauti Context shows you the Duplicate Value Rate per field: what percentage of records share their value with at least one other record. A field with a near-zero rate is a strong candidate for deterministic matching. A field where 60% of contacts share the same first name is a poor match and needs to be treated as a supporting attribute or investigated first.

That intelligence is what makes deduplication smarter. When you're ready to act, Plauti Deduplicate picks up where Plauti Context leaves off: natively, without leaving Salesforce.

Context closes the gap in two moves

Whether you're running classic automation today or rolling out AI agents next, the approach is the same.

Assess before you build

Before you activate a workflow or deploy an agent, run an analysis on the specific fields it depends on. The Analysis Summary gives you an at-a-glance read on fill rate, duplicate value rate, and other quality signals for every field, so you know what's reliable and what isn't before anything goes live.

Watch while it tunes

Schedule recurring analysis jobs daily, weekly, or monthly on your automation-critical and agent-critical fields. Context runs in the background, saves results automatically, and feeds them directly into trends and history. When a field starts to degrade, you see it at both the field and object level before you hear about it from a user. Set it once; Context keeps watching.

Who benefits from this?

For the team automating today

You don't have a data quality team. You have deadlines, a complex org, and a long list of things that could break if the underlying data isn't right.
Context gives you a native, inside-Salesforce way to check field health before you build on it, and to prove that health is holding after you deploy. No exports, no spreadsheets, no guessing.
The result: automations that run the way you built them, and a clear answer when someone asks why.
For the team adding AI next

Agentforce and other AI agents are built to reason over your data, summarize it, and act on it. What they don't do is question it. If a field is sparsely populated, inconsistently filled, or full of duplicate values, the agent works with that, and the output reflects it.
But complete fields alone aren't enough. Agentforce doesn't just need populated data; it needs to understand what those fields mean. Plauti Context Library gives every field a documented purpose and a named owner, so the agent has the context it needs, not just the value.
Context is the foundation layer. It gives every field documented metadata: a clear purpose, a named owner, a reliable score, so you know which fields are safe for AI to rely on, which need work first, and whether that's improving or degrading over time. That's not a nice-to-have before an AI rollout. It's the thing that determines whether the rollout goes well.

What changes when you can see this

Before Context, data problems surface after something breaks. After Context, they surface while there's still time to act.

That's the difference between a fire drill and a clean deploy. Between an agent that gives wrong answers and one that's actually useful. Between being blamed for a failure and being the person who caught it first.

Frequently Asked Questions (FAQ)

Does Plauti Context work with Agentfore?

Yes. Context is designed with Agentforce readiness in mind. It shows you, field by field and object by object, whether the data your agents depend on is complete, consistent, and reliably documented before you deploy, and on an ongoing basis after. The Context Library gives every field a documented purpose and owner, so your agents have the context they need, not just the value.

Which Salesforce objects does Plauti Context analyze?

Context works across standard and custom objects. You choose which objects and fields to include in each analysis job, and you can schedule those jobs to run daily, weekly, or monthly, so monitoring happens automatically without anyone needing to remember to run it.

How is this different from Salesforce's native field usage statistics?

Salesforce's native field usage tells you whether a field is being referenced in page layouts, reports, or code. Context goes further: it tells you the quality of the data inside the field: how complete it is, whether values are valid, what the duplicate value rate looks like, and how all of that is changing over time. The history view and trend analysis show you whether field health is improving, holding steady, or quietly degrading.

Does Plauti Context require data to leave Salesforce?

No. Plauti Context runs entirely inside your Salesforce org. No exports, no external pipelines, no data leaving your environment. All of it runs natively inside Salesforce. And that's not a footnote; it's the point.

Make your Salesforce automations and AI agents reliable

For Salesforce admins preparing automations or AI rollouts who need to know their data is ready before they build on it

Make your Salesforce automations and AI agents reliable

For Salesforce admins preparing automations or AI rollouts who need to know their data is ready before they build on it

The problem isn't your automation. It's what's underneath it.

Two signals. Two scenarios. One pattern.

Fill rate

Duplicate value rate

Context closes the gap in two moves

Assess before you build

Watch while it tunes

Who benefits from this?

What changes when you can see this