To build an agent that can listen, understand, and question customers to extract useful insights, conversational capability from LLMs isn't enough on its own. We need a harness.
A harness is what turns a horse’s raw power into useful movement. Without it, the horse runs, but it doesn’t go where it needs to go. The same logic applies to AI. Models are extremely powerful, but they need a harness to direct that power toward the right place and the results we’re after.
The agency mental model
When we present ReveLumi to prospective customers, we like to use the mental model of hiring a research agency. When we hire an agency, the first step is handing over a briefing.
That briefing carries context about the company and the problem, the research objectives, the hypotheses that need to be validated, and the decisions that depend on the outcome. It’s what gives the agent a reason to ask the most appropriate questions to reach the research objectives.
Beyond the general briefing, each respondent has an individual context field. The agent doesn’t treat every respondent as the same generic person. It walks into the conversation already knowing who’s on the other side, which changes the kind of questions it asks, how it interprets the answers, and how it steers the conversation.
Memory as a guardrail
Every generative AI has a temptation: when it doesn’t know something, it makes up a plausible answer. In a research interview, that’s the opposite of what we want. A hallucination becomes a false data point, and a false data point becomes a wrong decision.
That’s why we built a guardrail: when the agent doesn’t know the answer to something the respondent asks, it says it doesn’t have that information right now, that it will check, and keeps the interview going without freezing or making things up. The conversation continues, trust isn’t lost, and what gets recorded is real.
What the harness delivers
None of this shows up to the person being interviewed as “AI rules.” It shows up as a conversation that makes sense, that remembers what was already said, that knows when it doesn’t know. And to the person on the other side, receiving the final report, it shows up as something valuable: research that actually answers what needed to be answered.
The difference between using a generic AI to talk to customers and using an agent that listens to generate decisions isn’t in the model’s intelligence. It’s in the harness we use to guide the AI toward the results we need.
Want to see how this harness works in a real interview? Talk to our agent by answering our demo survey about how you choose what to watch on Netflix.
And if you’d like to put this agent to work researching with your customers, book a demo! We’d love to show you our agent and its harness.