The Data Layer – Why Agentic AI Needs Context, Not Just Records

29 July

This is Part 2 of our 5-part series on Agentic AI in Salesforce.

In this series, we’re unpacking what Agentic AI actually looks like in practice, starting with why it matters, then breaking down the layers required to make it work: data, process, execution, and readiness. If you missed Part 1, we covered why this shift is happening now and what makes Agentic AI different from traditional automation.

Let’s start with something that feels true for almost everyone: “our data is a mess.”

And yes, in many cases, it is. It’s noisy. Incomplete. Spread across systems.

But that’s not the core issue.

Most Salesforce orgs don’t have a data problem. They have a context problem.

Agentic AI is only as effective as the signals it can trust. It doesn’t guess. It reasons, based on what it knows about a customer, a case, a lead, or a product. And what it knows is shaped not just by the data you have, but how that data is modelled, validated, and connected.

Even “bad” data can become useful with the right structure and logic.

But if your data model is shallow, misaligned, or fragmented, your agent is flying blind.

Data Cloud is the foundation, but it is not the full story

Salesforce’s Data Cloud is now positioned as the connective tissue for agentic workflows. Identity resolution, real-time ingestion, calculated insights, these are the elements that give your agents memory and context.

But let’s be real. Buying Data Cloud does not mean you have usable data. Most businesses still need to:

Define what a unified profile actually looks like
Stitch identities across channels and systems
Choose which signals are real-time critical versus batch
Align segmentation with downstream agent actions
Validate trust and accuracy before delegation

Agents do not need all data. They need the right data, in the right structure, with the right freshness.

The signals that matter

If an agent is going to act on your behalf, you have to teach it what to trust. That means getting very deliberate about signals.

For example:

Is this person actually a decision-maker?
Has this customer already submitted a return request?
Is this case related to a safety event?
Has this prospect disengaged, or are they still in play?

These are not just fields in Salesforce. They are logic, thresholds, and metadata that need to be modelled and validated.

Agentforce lets you embed this logic using Prompt Builder and structured field validation. But if the underlying signals are noisy or incomplete, your agent’s logic will fail, even if the prompt is perfect.

What We Lead Out recommends

We typically start every Agentic AI build with a data layer assessment:

Signals Inventory – What do you currently track, and what matters to the agent?
Identity Graph – How well do you know who is who, across systems?
Trust Review – Can you delegate decisions based on this data?
Real-Time vs Batch Map – What must be current, and what can lag?
Readiness Score – Are you ready to build an agent, or do you need to clean house first?

Only once this layer is solid do we move into orchestration and execution.

What’s next

In the next article, we’ll break down the Process Layer, how automation, flow, and orchestration provide the structure that agentic systems plug into. You do not want an autonomous agent without clear swim lanes.