The five-layer AI stack
Agentforce, Trust Layer, Einstein Discovery, Einstein Studio, Data Cloud — what each one does and where it fits.
And the ones the slide deck oversells.
Customer writes "boiler banging in morning, smells weird, getting worried." A Match Skills work rule can't parse that. An LLM can — classify urgency, suggest fault category, package it for the optimiser. This is where Agentforce earns its keep cleanly.
Given 12+ months of asset failures, parts used, and Service Reports, Einstein Discovery learns failure patterns and predicts what will need replacing. The model doesn't replace the threshold rule — it adds nuance. Plan for retraining quarterly.
Reschedule explanations, dispatch reasoning, post-visit summaries — wherever the output is natural language, an LLM beats a template. Pair this with deterministic systems for the actual decisions.
Agentforce, Trust Layer, Einstein Discovery, Einstein Studio, Data Cloud — what each one does and where it fits.
Real primitives. Real Apex actions. No "AI dust" deployments.
An Agentforce agent is a system prompt + a set of topics + a set of actions. Topics define the conversation domain. Actions are Apex or Flow methods the agent can invoke. We design the topic taxonomy carefully — too narrow and the agent rejects valid intent; too broad and it hallucinates.
Atlas orchestrates the LLM calls. The Trust Layer masks PII before prompts leave the platform, applies content moderation, and grounds responses in your Salesforce data — not public training corpora. Audit trail every interaction. Latency 1–4s per agent step; architect with timeouts.
No-code regression / classification on your Salesforce data. Predicts failure probability, parts demand, churn risk, ticket priority. Needs ≥12 months of clean training data; needs a quarterly retraining cadence; needs a precision target you accept (most teams settle around 80%).
For ML you already own — Databricks, Amazon SageMaker, Vertex AI. Surface predictions inside Salesforce records. Heavier than Discovery but the right fit when your data scientists own the model.
Unifies customer + asset + telemetry data across systems into one profile. Real-time activations push the right signal to Service Cloud, FSL, and Marketing Cloud. Often the foundation an AI strategy actually needs.
Agentforce is billed per action. High-volume dispatch flows can rack up thousands of actions/day. We forecast the cost before we architect the flow — and recommend supervised-not-autonomous patterns where the math says so. The honest dispatching architecture is here.
And when it isn't.
Three engagement modes.
30 minutes, no demo, no deck. We'll tell you whether Agentforce + Einstein will pay back on your data — or whether you should fix the foundation first.
Book a 30-min review