Are You Marketing to Bots Now? What Agentic AI Means for Your Content Strategy

It started with a LinkedIn comment. Not even a post — a comment buried in a thread sparked by the Notion CEO's take on vibe-coded CRMs. But the question it raised has been rattling around in our heads ever since: if agentic AI is making the buying decisions, who exactly are we marketing to? The answer, uncomfortable as it is, might be: both. Humans and their automated proxies. And if that sounds like a plot twist you weren't prepared for, you're not alone — but it's time to get ready.

We recently sat down with Ashleigh, our resident MarTech researcher, and Lauren, our SEO and brand strategist, to pull apart what's actually happening in the space right now, what the research says, and what practical moves marketers need to make. What follows is a distillation of that conversation — along with the context, implications, and action items that matter most.

What Agentic AI Actually Is (And Why It's Different from LLMs)

Let's be precise about terms, because the conflation here matters. Large language models (LLMs) are the engines that power tools like ChatGPT and Claude — they generate responses based on patterns in vast text datasets. Agentic AI is something different: it takes actions. It makes decisions. It executes tasks autonomously, often in sequence, without a human signing off at each step.

As Ashleigh put it in our conversation: "These agentic AI models are now making the buying decisions — either all the way to purchasing, or compiling the information for a human to make the final decision." The example that keeps surfacing is Amazon's AI shopping agent, which can be granted full purchasing authority by consumers who trust it to buy within their stated preferences. That's already B2C reality.

In B2B, the entry point is procurement. Agentic systems are being deployed at the discovery and shortlisting phase — doing the first pass on vendor research that used to fall to a human. A 2025 report from McKinsey on AI in B2B buying found that autonomous agents are increasingly being piloted for supplier identification and comparison tasks, with procurement among the earliest adoption areas. The implication is stark: if your content isn't readable by those agents in the way they expect, you won't make the shortlist. Not because a human dismissed you — because an algorithm never surfaced you.

This isn't theoretical. This is the procurement stack your buyers are building right now.

The SEO Parallel: We've Been Here Before

Here's what reframed our whole conversation, courtesy of Lauren: we've already lived through a version of this disruption. When SEO content emerged as a discipline, creative writers had to reconcile something uncomfortable — the content they were pouring their craft into wasn't written purely for readers. It had to pass through a technical filter first.

Lauren's analogy was sharp: "It's like applying for a job — you have to get past the ATS before a person actually looks at your resume." SEO has always been about satisfying a machine layer before reaching the human layer. And the quality divide that emerged was real. Bad SEO content — keyword stuffing, hollow filler — technically passed the filter but was immediately dismissed by human readers. Good SEO content did both: it satisfied the algorithm and served the reader.

Agentic AI creates the same fork in the road. You can write content that technically exists on your website, or you can write content that is discoverable, comparable, and actionable by the systems now doing the pre-qualifying work for buyers. As Lauren noted: "There's bad SEO content and there's AI slop — and a similar thing is going to happen with AI. The tactics without the substance will get through sometimes, but humans will disregard it." The standards for the latter are higher — and more specific — than most brands currently meet.

We've written about building content that serves both search engines and human readers over at Winsome's SEO content hub. The principles there aren't going anywhere. They're just getting a new application.

Clarity Over Cleverness: What Agents Actually Read

This is where the conversation got pointed. There's a particular brand pathology that Ashleigh described with precision — the tendency to wrap services in so much proprietary language, coined phrases, and aspirational abstraction that the actual offering disappears. "We champion the blah blah blah. We innovate and iterate and achieve more." We've all read that website. A lot of us have written it.

Agents don't read between lines. They don't infer. They don't appreciate the Marvel cinematic quality of your brand architecture. As Ashleigh put it: "No AI system has any idea what any of that means. And if your product descriptions don't state it clearly, you're gone. No retrieval mechanism is going to be able to interpret that for you."

The specific failure mode here is branded terminology that has no semantic cousin in common language. If you've named your software suite something evocative but opaque — say, a series of "Be" words that internally map to data integrity functions — an agent comparing you to competitors will have no framework for equivalency. Lauren made this concrete: "If your feature is the equivalent of what your competitor has, but it's called something totally different, there has to be some consistency across. Maybe that's having a comparison chart with the more common language term, so it comes up in those comparisons."

This is a practical mandate, not a creative death sentence. Lauren's position was nuanced: "Services have to be the most plain out of anything. But if you want a larger brand identity that says something different, I still think that's okay — you just have to back up the flair." The homepage can be evocative. The service pages must be surgical.

The Structural Play: llms.txt, Schema, Pricing, and URL Integrity

Content clarity is one side of this. Site structure is the other, and it's where most businesses are losing the game before the content even gets read.

Ashleigh and Lauren broke down the specific structural elements that matter most right now. Schema markup and structured data are foundational — not new, but chronically absent. "When we take on a new client and review their website, it's often not there," Ashleigh noted. Service-type schema, product descriptions, and all associated metadata need to be present on every relevant page, not just the homepage.

Pricing transparency came up with particular urgency. The argument is simple: when an agentic AI compiles vendor comparisons, pricing is one of the primary comparison criteria. If your pricing isn't findable, you don't make the chart. "You don't have to have the exact price," Ashleigh said, "but at least a range so that you show up in that comparison." We've been telling clients to publish pricing for years. The stakes just got higher.

Lauren also raised llms.txt — a relatively new file format that functions like a sitemap for AI crawlers, allowing you to set prioritization rules for how LLMs read and surface your content. You can instruct crawlers to prefer service hub pages over blog posts, to organize content by topic hierarchy, and to follow your preferred URL taxonomy. "It's one of those things where it's just a little extra piece where you can add structure into your website so that when the LLM goes to crawl your website, it's getting some clues on what you would like it to see," Lauren explained.

URL structure itself deserves its own conversation — and we'll have it. But the short version: if your URL says one thing and your content says another, no machine will connect them. Clarity starts at the address bar.

The Consistency Imperative: How AI Decides Who Shows Up

There's a version of this problem that feels impossible — how do you control what an AI says about you when you can't control the AI? Lauren's answer was reassuring and demanding in equal measure: consistency.

"If you have consistency across all of your platforms — your website, all communities, all social, affiliate sites, whatever — if you're saying the same exact thing, that is how you will show up." This is how LLMs build a picture of a brand. They're pattern-matching across sources. If the pattern is coherent, the picture is clear. If every channel says something slightly different, the model shrugs and moves on to someone whose signal is stronger.

This is also where case studies and external reviews stop being "nice to have" and become structural requirements. Ashleigh's research was direct on this point: "Independent reviews are one of the criteria for agentic AI — not putting your own testimonials on your site, but pulling in from another source that is embedded on your site." LLMs weight third-party validation significantly higher than self-reported claims. If you say you're excellent, that registers as noise. If five external sources confirm it and link back to you, that registers as signal.

A 2024 study on LLM citation behavior from researchers at Princeton and MIT found that language models systematically favor content that is externally corroborated and consistently referenced across multiple domains — which maps almost perfectly to traditional domain authority principles in SEO. The mechanisms are different, but the underlying logic holds: earn trust through third-party endorsement, not self-promotion.

The corollary for anyone tempted to coin new industry terminology: Lauren's hypothesis is that you can still build a proprietary vocabulary, but it requires "massive, massive amounts of scale" before the internet catches up. For most brands, that's not a viable near-term bet. Build the common language foundation first. Earn the right to your own lexicon later.

Both/And: Marketing to Humans and the Systems That Brief Them

Here's where we landed, and it's the framing that actually helps. This isn't a choice between marketing to humans or marketing to agents. It's a both/and — which, as Ashleigh acknowledged, "makes it that much harder." But Lauren's reframe is the one worth keeping: "You're just putting the content in a vehicle. Just like we did with SEO, you're shaping the content in the format that's needed to get it to the human who's going to then benefit from it."

The practical checklist that emerges from this conversation is shorter than it feels. Make your service pages plain-language clear. Publish pricing. Add schema markup. Build your llms.txt file with priority rules. Audit your URL structure. Accumulate external reviews and case studies on third-party platforms. Maintain consistent language across every channel. Test what the major LLMs currently say about your brand, and note the gaps.

That last item is underrated. Lauren is deep in research on tools that measure how brands show up in AI search — and that's a conversation we'll bring you in a future session. But for now: go ask ChatGPT, Claude, and Perplexity what they know about your company. What they say, and what they miss, is a diagnostic.

The buyer's robot is already doing its homework. Make sure you've done yours.

Ready to make sure your content speaks to both the humans and the systems briefing them? Winsome's content strategy team builds digital presence that works across every layer of the modern buyer journey — from LLM retrieval to human conversion. Let's talk about your content strategy.