Something significant is beginning to take shape in how products get discovered and recommended. More people are delegating research to AI assistants - asking ChatGPT to compare mortgage providers, getting Perplexity to shortlist running shoes, having Gemini draft a supplier recommendation. The human is still the final buyer, but the AI is doing the evaluation work. That shift alone demands a rethink of how brands communicate.
But there is a further step coming. As Ahrefs recently observed, once AI agents become the layer between people and the internet, marketers will not just need to convince a human. They will need to convince another AI. That is agent-to-agent marketing - and it changes the fundamental unit of persuasion.
The Audience Has Changed. The Brief Hasn't.
Most marketing is still written for humans. That means emotional hooks, aspirational language, social proof designed to resonate with a person reading a page. That approach is not wrong, but it is increasingly insufficient. When an AI agent is doing the research on a user's behalf, it is not responding to emotional framing. It is parsing structured information, evaluating consistency, and drawing on what it already knows about a brand from multiple sources.
The implication is that brands need content that works on two levels simultaneously. It should still be readable and compelling to humans - because humans remain the ultimate decision-makers. But it also needs to be clear, factual, well-structured, and citable, because an AI agent needs to be able to extract a confident recommendation from it. If your product page is vague about specifications, pricing structure, or key differentiators, a cautious AI agent will simply move on to a competitor whose content is more legible.
What an AI Agent Actually Evaluates
When an AI agent researches a product category on a user's behalf, it is not running a search in the traditional sense. It is synthesising across multiple signals - web content, reviews, structured data, brand mentions in authoritative sources, and its own training knowledge. The output is a recommendation, not a list of links. That means your brand either makes it into the synthesis or it doesn't.
Factors that influence whether an AI agent cites or recommends a brand include: the clarity of its positioning, the consistency of claims across multiple sources, the presence of verifiable specifics (not vague superlatives), and whether authoritative third parties have written about the brand in a way that corroborates its own claims. This is not entirely different from what Google AI Overviews look for - but the stakes are higher when the output is a direct recommendation rather than a ranked list.
One practical consequence: brands with thin or inconsistent digital footprints will struggle. An AI agent trying to verify whether a product is genuinely well-regarded will look for signals beyond your own website. If independent coverage is sparse, if review platforms contradict your marketing claims, or if your content is structured around keyword density rather than genuine usefulness, you are invisible to the evaluation process.
Structured Content Is Not Optional at This Stage
The shift towards agent-mediated discovery makes structured, machine-readable content a strategic priority rather than a technical nicety. Schema markup, clear FAQ sections, explicit comparison information, and well-organised product or service pages all help an AI agent extract accurate information efficiently. An agent that cannot confidently parse your content will not cite you - it will cite whoever made its job easier.
This matters particularly for UK businesses operating in categories where purchasing decisions are research-heavy - financial services, B2B software, professional services, healthcare products. These are precisely the areas where consumers are most likely to delegate initial research to an AI assistant. If your content cannot hold up to agent-level scrutiny, you lose the opportunity before any human even sees your brand.
The Paid Search Dimension
It would be a mistake to treat agent-to-agent marketing as a purely organic or content-side challenge. Paid search is implicated too. As AI agents become more capable and more integrated into browsers, operating systems, and third-party apps, the question of whether paid placements get surfaced to agents - or influence their recommendations indirectly - is already being asked in product teams at Google and Microsoft.
For now, the most direct paid implication is that your Performance Max and Demand Gen campaigns are generating brand signals - impressions, engagement, brand search volume - that feed into the broader ecosystem of signals an AI model draws on. A brand that runs no paid activity is not just missing direct conversions. It may be reducing its overall signal strength in the very data sources that LLMs and AI agents use to form impressions of category authority.
This does not mean spend more for the sake of it. It means being thoughtful about whether your paid activity is building a consistent, coherent brand presence or simply generating transactional noise. Campaigns that drive branded search, reviews, and meaningful engagement have downstream value in an agent-mediated world that purely performance-focused campaigns do not.
What Brands Should Actually Do Right Now
The first practical step is an honest audit of how clearly your brand and products can be understood by a system that has no prior knowledge of you. Pick your most important product or service and ask: if an AI agent were trying to recommend this to a user with a specific need, does your content give it everything it needs to do so confidently? Specifics, comparisons, evidence, and context - not marketing language.
The second step is to treat third-party presence as infrastructure. Coverage in industry publications, accurate listings in relevant directories, consistent information across review platforms, and clear authorship on expert content - these are not vanity metrics. They are the corroborating signals that allow an AI agent to trust what your own site says about you.
Agent-to-agent marketing is not a distant scenario. It is the logical extension of the AI search behaviour that is already reshaping how brands get discovered. The brands that prepare for an AI-mediated audience now - by making their content clearer, their claims verifiable, and their digital presence consistent - will be the ones that get recommended when the agent makes its call.