What this is
One screen showing how a messy, five-system customer estate becomes an
agent-ready grounding set: Data Cloud ingests and resolves identities, a
mastering layer applies quality gates and survivorship, predictive models score the
clean profiles, and Agentforce acts on what survives — with the
Einstein Trust Layer auditing every answer. Data is synthetic; the architecture is the point.
Reading the flow
Red = blocked from agent use until fixed (field conflicts, dead contact points).
Amber = held on purpose (missing fields, consent).
Green = trusted and flowing to agents.
Blue = resolution machinery doing its job.
Click any band or node — the records table filters to the accounts behind it, and the
stage card names the exact Salesforce component that implements it.
The three stories to tell
- Mesa Machine Works — duplicate from the acquired org; Sales Cloud says
“Approved,” SAP says “On Hold, $48k past due.” Open Ask Agentforce and show the same
question answered both ways. This is the whole thesis in one screen.
- Bravada Contracting — consent revoked in Marketing Cloud; the gate suppresses
agent marketing actions automatically. Trust is enforced, not documented.
- Cedar Ridge Fabrication — churn model (trained on mastered profiles)
flags 0.91; the agent drafts outreach citing two escalated cases; a human approves in
Slack. Predict + act, closed loop.
Q & A you may get
- Is this running on real Agentforce?
- The steward agent is simulated — my own reasoning loop posting to Slack — and it’s
labeled as such. The seams are exactly where the real components plug in: the detection
topics become Agent Builder topics, the reasoning call becomes Atlas grounded via the Trust
Layer, the Slack card is the Agentforce-in-Slack surface, and the approval callback is a
custom agent action writing back through Data Cloud.
- Where does the Informatica acquisition fit?
- It’s the strongest possible confirmation of the thesis: Salesforce bought an MDM +
data-quality + governance company because agents need mastered data. The mastering layer in
this demo is exactly the capability Informatica brings first-party — survivorship, quality
rules, catalog and lineage — and my career has been competing with and against that stack,
so I know where it lands well and where it needs architecture around it (match tuning,
survivorship politics, steward adoption).
- Doesn’t Data Cloud identity resolution already solve this?
- It solves matching. It doesn’t decide which system wins when merged sources
disagree on credit status, doesn’t score trust, and doesn’t route borderline cases to a
human. That survivorship-and-governance layer is what I’ve built for a living — expressed
here natively, no third-party MDM box in the diagram.
- Why do stewards approve in Slack?
- Because that’s where people already are, and Slack is Salesforce. A 30-second approval
in the flow of work beats a data-quality backlog review every quarter.
- Why does the churn model care about mastering?
- A model trained on duplicates is garbage-in twice — once at training, once at inference.
Cedar Ridge scores 0.91 as one customer; as three fragments it never crosses a threshold.
- What’s the KPI?
- The Trust Score (top right): five weighted dimensions over the whole estate. It’s the
number a CDO can put in a board deck — and the roadmap commits to moving it 71 → 90.
Good to know
This page: agent-force.cuppy.io · deck via ▶ Strategy deck ·
theme toggle ◐ persists per browser. All figures modeled on the synthetic
Ironclad scenario; labeled accordingly. Built by Benny Garner as interview material —
not an official Salesforce property.