Why Agentforce outputs are bland — prompt and data fixes

You spent the budget. You bought the licences. You watched the demo. And now Agentforce is sitting in your Service Cloud answering customer questions with the personality of a damp tea towel.

It's the most common complaint we hear from clients six weeks into an Agentforce rollout. The outputs are technically correct, factually fine, and entirely forgettable. They sound like every other generative AI product on the market because that's exactly what's happening underneath: the model is falling back on its training data instead of yours.

The good news is that the fix is rarely the model. It's almost always one of three things, and usually all three at once.

It's the prompts

Out of the box, Agentforce ships with generic system prompts. Salesforce wrote them to work for everyone, which means they work brilliantly for nobody. If you haven't customised them for your business, your tone of voice, and your specific use cases, the agent will sound like a chatbot in a blazer.

Three things to do here:

• Write custom prompt templates per topic. Don't rely on the Standard Prompt Templates. Create your own with tight guardrails, specific examples, and the language your customers actually use.
• Encode your brand voice explicitly. "Reply in a friendly, professional tone" gets you nothing. "We say 'order' not 'purchase'. We never use exclamation marks. We address customers by first name only after they've used theirs first." That gets you something.
• Give it grounding examples. Three or four real exchanges from your support team will do more than a thousand words of style guide.

The single biggest mistake we see is teams treating prompts as a one-shot exercise. They write them in week one, deploy, and never touch them again. Prompts are living documents. Plan to revisit them monthly for the first six months.

It's the data

An agent is only as good as the data it can see. If your Salesforce org is the usual mess — duplicate accounts, missing fields, contact records with no email, opportunities still open from 2019 — the agent has nothing useful to work with. So it improvises, and improvisation in customer service sounds like waffle.

What we look at on every Agentforce engagement:

• Duplicate accounts and contacts. If the agent retrieves three versions of the same customer it will pick one at random and confidently quote irrelevant history.
• Empty or stale fields. Industry, employee count, last contact date — if these are blank, the agent can't personalise.
• Missing knowledge articles. If your Knowledge base hasn't been touched since 2022, your agent is answering 2025 questions with 2022 answers.
• No Data Cloud grounding. Agentforce gets dramatically better when it can pull from Data Cloud. If you're not using it, you're handicapping the agent before it opens its mouth.

You don't need a six-month data project to see improvement. A focused two-week tidy of the top 500 active accounts and the most-referenced knowledge articles usually shifts the needle visibly.

It's the topics

Topics in Agentforce are the buckets of work the agent knows how to do. Most rollouts start with two or three topics covering 80% of inbound queries. Then the agent gets asked something outside those topics, falls back to its general LLM brain, and produces something bland and slightly wrong.

Audit your topic coverage every two weeks for the first three months. Look at the conversation logs, find the questions the agent answered weakly, and either add a new topic or extend an existing one with more actions. Actions are where the value is — they let the agent actually do something rather than just talk about it. An agent that can describe how to reset a password is interesting. An agent that can reset the password is useful.

What good looks like

On a typical Agentforce engagement we'll spend the first week on data hygiene before we touch a prompt. It feels counter-intuitive when you've just bought an AI product, but it's where the lift comes from. We usually find a fix-the-data-then-fix-the-prompts split of roughly 60/40 effort, and the teams that resist that split are the teams that end up disappointed.

The pattern that works is unglamorous: dedupe the accounts that the agent is most likely to retrieve, fill in the empty fields on the records that matter, refresh the knowledge articles the agent will lean on, then — and only then — rewrite the prompt templates with your tone of voice and your real examples. Add Data Cloud grounding where it earns its keep, ship two or three well-scoped topics rather than ten weak ones, and review the conversation logs every fortnight for the first quarter.

Teams that ship Agentforce well treat prompt templates as products — they version them, they measure them, they iterate. The agents that get rated “polite but useless” in week six tend to be the ones whose prompts haven't been touched since go-live.

What to do next

If your Agentforce outputs are bland, don't blame the model. Audit the prompts, audit the data, audit the topics. In that order.

We do this as part of our Agentforce engagements, and our Salesforce Health Check includes a quick read on whether your data is in shape for AI before you commit to anything bigger.

It's a fixable problem. Most of the time it's a fortnight's work, not a re-platforming exercise.