Would you let an AI agent spend $50,000 of your company’s budget without checking its work?
Probably not.
Right now, our marketing world is distracted. We’re busy arguing over AEO/GEO strategies, heck, even the acronym AEO/GEO. And on the paid side, we’re all obsessing over how OpenAI might place banner ads inside ChatGPT.
It’s time to move the conversation from “How do I optimize my website for an LLM?” to “How do I optimize my brand for an autonomous agent?”
The real shift is about who makes the decision. As we move toward agentic commerce – a world where AI actively evaluates options, recommends vendors, and completes purchases on our behalf – we need to focus on answering “Why would an AI agent trust us enough to recommend us at all?”
The Trust Architecture Of AI Agents
If AI agents are going to start making purchasing decisions, we might think capability is the big hurdle. But the biggest hurdle is trust.
A new paper by Stefano Puntoni, Erik Hermann, and David Schweidel from Wharton breaks down how to design AI agents people actually rely on. Their core point is trust comes from helping the customer manage uncertainty.
They outline three components. Look at them through a marketing lens, and they double as a blueprint for becoming “recommendable.”
1. Reasoning And Goal Alignment
To reduce “pre-action” uncertainty, an agent has to understand the user’s goals and be able to explain why it chose a particular option.
Marketing takeaway: An AI won’t recommend a brand it can’t defend to the human on the other side. It needs to surface clear reasons, trade-offs, risks, and biases.
That means your materials can’t just be persuasion. You need solid, checkable facts: clear pricing, realistic implementation timelines, honest limitations, and real comparative advantages.
2. Action And Feedback
Agents also need to show what they’ll do and how user input changes their behavior – what the authors call “feedback on feedback.”
Marketing takeaway: Agents will favor vendors with clean, predictable execution paths. If understanding how your product works requires three sales calls and a gated PDF, you’re at a disadvantage versus a competitor with open docs, transparent onboarding, and clear next steps.
3. Interface And “Anti-Sycophancy”
Most systems today are trained to be agreeable – to mirror the user and say what they think the user wants to hear. The Wharton team argues that, for calibrated trust, agents actually need to push back: Ask clarifying questions, surface edge cases, and sometimes say “no.”
Marketing takeaway: A serious agent will behave more like a consultant than a yes-man. It will probe: budget, constraints, compliance, integration needs. Your brand needs enough depth – FAQ content, implementation detail, nuanced comparisons – to stand up to that kind of questioning.
Why Trust Becomes A Ranking Factor: The Risk Transfer
The heart of the agentic shift is who carries the risk.
In classic search, the platform carries fairly little risk. You search for a CRM, click a result. If you buy a terrible product, your frustration is with the vendor, not the search engine.
Once you delegate a decision to an AI agent, that changes.
If an agent independently evaluates, selects, and implements a $50,000 CRM that turns into a disaster, the user loses trust in the vendor and in the agent.
Because an agent must justify its recommendation, it will systematically favor vendors it can explain and not just vendors that rank well.
And because an agent’s survival depends on being trusted, it will likely get very conservative, very fast. It can’t afford to gamble on shaky brands or thin evidence.
It won’t recommend you because you wrote clever copy or “won” an SEO trick. It will recommend you because, with the information it has, you are the safest, most defensible choice.
Trust – grounded in evidence and consensus – starts to behave like a ranking factor. This is calibrated trust – confidence proportional to the strength, consistency, and verifiability of the evidence surrounding your brand.
From Visibility To Eligibility
This changes how we think about success.
Recent work from Rand Fishkin and SparkToro shows that if you ask AI systems for brand recommendations repeatedly, you get wild variance: different brands, different orders, different list lengths. Treating “AI rank” like SEO rank is measuring noise.
But inside that noise is something stable: a core consideration set. Across many runs, the same handful of brands show up again and again. Those are the vendors the system sees as safe to put in front of a user.
You’re now optimizing for eligibility, on top of visibility.
What Marketers Need To Do Differently
Shift from “catch attention” to “prove reliability”:
1. Make Your Data Legible
Design for machines as well as humans. Clean product data, structured specs, accessible APIs or feeds, and sensible site architecture are table stakes. If an agent struggles to parse what you sell, you’re easy to skip.
2. Remove Avoidable Ambiguity
Stop hiding basic facts – pricing bands, SLAs, integration requirements – behind forms. If an agent needs those details to justify a recommendation and can’t find them, it will move on to a vendor that’s more transparent.
3. Strengthen External Validation
Agents lean heavily on consensus to reduce risk. That makes third-party proof more important: customer reviews, active communities, independent tutorials, analyst notes, credible press. The more real-world signal around you, the easier you are to defend.
4. Build For “Show Your Work”
Help the agent make its case. Comparison tables, return on investment models, case studies with numbers, “best for X” guidance – all of these become building blocks the agent can reuse when it explains to a buyer why you made the shortlist.
Read More: How AI Is Reshaping Who Gets Recommended: Marketing In The Eligibility Era
The New Mandate
We’re heading into a world where the search bar is less “type and browse” and more “ask and it’s handled.”
In the visibility era, your job was to catch a person’s eye.
In the eligibility era, your job is to ensure the systems acting on their behalf feel confident choosing you.
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Featured Image: Krot_Studio/Shutterstock
