Skip to main content

Marketing Cloud AI: Style Guide

The opinionated rules Cleon applies to AI in Marketing Cloud — when to reach for Einstein, Agentforce, or an external model, the guardrails each one needs, and the 'Agentforce vs external AI' decision in full. The discipline document that ties the AI subcategory together.

Decision framework·Last updated 2026-06-01·Drafted by Lira · Edited by German Medina

This is the page where Cleon stops describing what the AI surfaces are and starts saying what we do with them. The reference pages document each surface — Einstein, Agentforce, external models from CloudPages. The gotchas document where each one bites. This Style Guide is the discipline that decides which AI to reach for, and the guardrails that make any of them safe to ship.

The anchor decision — Agentforce vs. an external model — is the one teams get wrong most often, so it gets its own section. The rules are short on purpose; when a rule needs an explanation, the explanation is in the page it links to.

The first question: which surface, for which job?

AI in Marketing Cloud answers three different questions. Match the job to the surface before anything else.

| The job | The surface | Why | |---|---|---| | Predict who and when — engagement, send time, best asset | Einstein | Trained on your own engagement history; already in the platform | | Answer over customer data, or take a governed action | Agentforce | Grounded on Data Cloud, runs in the security model, built to act | | Generate or classify self-contained language | External LLM | A general model you call; knows only what you send, acts only when wired |

Reaching for the wrong surface is the most expensive AI mistake — an external model bolted onto a job Einstein already does, or a render-time LLM call doing what an Agentforce agent should govern.

The anchor decision: Agentforce vs. an external model

Both are "AI that produces language," which is why they get confused. They are not interchangeable. The decision turns on three questions:

1. Does the task need your customer data?

  • Yes, and a lot of it → Agentforce. It's grounded on the Data Cloud profile by design; you don't ship data anywhere.
  • No, or only a little you can scope → an external model is viable. You control exactly what leaves in the prompt.

2. Does the task take an action in the platform?

  • Yes — trigger a journey, update a record, message a customer → Agentforce. It runs in the security model with governed, auditable actions.
  • No — it just produces text you store and use → an external model is simpler. Generate ahead of time, write to a DE.

3. Who owns the data-governance surface?

  • You want the platform to own grounding and security → Agentforce. The data stays in Salesforce.
  • You accept owning the data-egress decision → an external model. Now a DPA, redaction, and retention are your responsibility (see gotchas — gotcha 6).

The short version: Agentforce when the task needs your data and/or needs to act, governed by the platform. An external model for self-contained language tasks where you don't want to hand a platform agent your data and your actions. When both could work, prefer the one whose failure mode you'd rather own.

Guardrails by surface

Einstein

  • Check the engagement history under any score before you trust it; a thin history scores noise. (Gotcha 1.)
  • Don't put an STO send on a hard deadline — it moves the send across a day. (Gotcha 2.)
  • Treat Content Selection and Copy Insights as suggestions a human approves, never auto-ship. (Gotcha 3.)

Agentforce

  • The agent is only as good as the Data Cloud model under it — run the agent-readiness check first. (Gotcha 4.)
  • An agent that can act gets the automation guardrails: approval, blast radius, audit trail, kill switch. (Gotcha 5.)

External models

  • Ahead of time, not at render time — generate into a DE, never call synchronously in the send path. (Gotchas 6–7.)
  • Never hard-code the API key; read it from a restricted DE or Key Management.
  • A data-processing agreement and a redaction rule before any PII leaves the platform. (Gotcha 6.)
  • A fallback value on every failure — never leave a field blank when the model is down. (Gotcha 7.)
  • A throttle and a cost model against the real audience size. (Gotcha 9.)
  • Validate, then have a human approve open-ended output before it ships. (Gotcha 8.)

Patterns to prefer

  • The surface that's already in the platform, when it does the job — Einstein over an external model for prediction; Agentforce over a hand-rolled agent for data + actions.
  • Generation ahead of time, cached in a DE — turns a fragile render-time dependency into a fast, deterministic lookup.
  • A fixed set of options the model chooses among, over open-ended generation, whenever the job allows.
  • A human approval gate on anything customer-visible an AI produced or selected.
  • The agent-readiness check passing before any agent reads marketing data.

Patterns to refuse

  • A render-time external model call in a send path — someone else's p99 is now your page's. (Gotcha 7.)
  • An Einstein score driving an audience on a thin history. (Gotcha 1.)
  • An agent grounded on a fragmented model — confidently wrong is worse than "I don't know." (Gotcha 4.)
  • An agent that can act without a kill switch. (Gotcha 5.)
  • PII in a prompt before a DPA covers it. (Gotcha 6.)
  • A generated field shipped on a three-preview spot check. (Gotcha 8.)
  • "The AI did it" as an answer to who's accountable. (Gotcha 10.)

The accountability check before any AI surface ships

AI doesn't move the accountability to the model. Before any Einstein score, agent, or generated field touches a customer, confirm:

  • [ ] A human is accountable for what this surface produces, and can explain and defend it.
  • [ ] The right surface is matched to the job (predict / act-on-data / generate).
  • [ ] The surface's guardrails above are in place for the surface in use.
  • [ ] Anything customer-visible and open-ended has a human approval gate.
  • [ ] For external models: a DPA covers the data, and there's a fallback on failure.
  • [ ] For agents: the agent-readiness check passes and actions are governed.

If any box is unchecked, the AI isn't ready to ship — and "the model decided" won't be an acceptable answer when it's wrong.

Related

If you spot a rule missing — or one of these rules being violated in our public work — write to hello@wearecleon.com. We add it, or we fix it and we say so.