{"id":5026,"date":"2026-04-09T13:20:00","date_gmt":"2026-04-09T13:20:00","guid":{"rendered":"https:\/\/apadmi.com\/insights\/why-the-buy-side-of-agentic-commerce-is-the-problem-nobodys-solving\/"},"modified":"2026-07-23T11:47:31","modified_gmt":"2026-07-23T11:47:31","slug":"why-the-buy-side-of-agentic-commerce-is-the-problem-nobodys-solving","status":"publish","type":"insight","link":"https:\/\/www.apadmi.com\/en-nl\/insights\/why-the-buy-side-of-agentic-commerce-is-the-problem-nobodys-solving\/","title":{"rendered":"Why the buy side of agentic commerce is the problem nobody\u2019s solving"},"content":{"rendered":"\n\t
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\n\t\n\t\tWhy the buy side of agentic commerce is the problem nobody\u2019s solving\t<\/h1>\n\n\t\n\t<\/div>\t<\/div>\n<\/div>\n\n\n\t
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\n\t\t\tThere\u2019s a conversation happening across every large commercial organisation right now about agents. It goes something like this: agents are coming, we need an agent strategy, how do we make sure agents find us? That instinct is right, but the sequencing is wrong \u2014 and the sequencing matters, because getting it backwards means building the wrong thing at the wrong time for the wrong reason.\t\t<\/p>\n\t\n\t\n\t

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\n\tLet\u2019s explore the landscape as it actually stands today, rather than how it appears in strategy decks.<\/p>\n\n\n\tWhat\u2019s deployed today is chatbot commerce, and it comes in two flavours<\/h2>\n\n

\n\tIn the platform-mediated version, a user talks to ChatGPT, Gemini, or Claude and asks it to find a flight. The LLM recommends something, a protocol stack connects that recommendation to the supplier\u2019s booking system, the transaction completes without the user leaving the chat window, and the LLM provider takes a referral fee. This is Google search ads rebuilt for a conversational interface \u2014 the user thinks they have an agent, but what they actually have is a shop assistant who works on commission.<\/p>\n\n

\n\tIn the retailer-embedded version, the chatbot lives inside the retailer\u2019s own app or website. The user talks to the airline\u2019s chatbot, it populates the basket, the user checks out within the airline\u2019s own surface. No intermediary, no referral fee, and the brand relationship is preserved. This is a UX improvement rather than a channel shift \u2014 a legitimate defensive move against the platform-mediated model.<\/p>\n\n

\n\tLook under the hood of either version and the \u201cagentic\u201d part evaporates. When ChatGPT \u201cbuys\u201d something from a retailer, it\u2019s calling a structured API \u2014 product discovery, cart management, checkout endpoints. OpenAI\u2019s Agentic Commerce Protocol has four REST endpoints. That\u2019s a well-designed shopping cart interface, not agent-to-agent negotiation \u2014 the retailer is running a shop with an API. A significant amount of protocol work (ACP, UCP, and the associated product feed standards) is going into making seller platforms directly visible and queryable by the chat platforms, which makes this model work better but doesn\u2019t make it agentic.<\/p>\n\n

\n\tThese are necessary first steps. The emerging protocol infrastructure (Google\u2019s A2A, Anthropic\u2019s MCP, Visa\u2019s TAP, Mastercard\u2019s Agent Pay) is genuinely designed for the full spectrum, from chatbot checkout to autonomous agent-to-agent negotiation. But the current commercial deployments are all at the chatbot end, because that\u2019s where the users are today.<\/p>\n\n\n\tWhat\u2019s coming next is the proliferation of personal buyer agents<\/h2>\n\n

\n\tThis is the step that changes everything. Not sell-side agents or new protocols \u2014 buyer agents. Autonomous software acting on the user\u2019s behalf, with its own tools, its own information sources, no channel loyalty, and no inherent reason to complete a transaction in any particular place.<\/p>\n\n

\n\tThe demand signal is already clear. OpenClaw \u2014 a solo developer\u2019s side project, vibe-coded and genuinely painful to configure \u2014 went from launch to 180,000 GitHub stars to OpenAI acqui-hire in sixty days, because it actually controlled applications, booked flights, managed inboxes, and completed tasks across platforms. If people are willing to wrestle with day-zero open-source cruft to get a working personal agent, companies will ship polished versions given time. OpenAI hired the developer behind OpenClaw – the direction is unmistakable.<\/p>\n\n

\n\tWhen these agents do arrive, they won\u2019t limit themselves to the platform-mediated chatbot commerce pipe. They\u2019ll use your API if that\u2019s the best route, your website if there\u2019s a promo code the API doesn\u2019t honour, a consolidator if the net fare is lower. They\u2019ll have unlimited patience, real-time access to competitor pricing, and no commitment to completing a transaction where they started the conversation. They\u2019ll compare and shop across all your channels, and pick the one that suits their user best \u2013 regardless of your channel strategy.<\/p>\n\n\n\tThat buyer-side pressure is what will actually justify building non-trivial sell-side agents<\/h2>\n\n

\n\tNot the generic \u201cwe need an agent strategy\u201d from a conference deck, but a specific commercial need. When buyer agents start collapsing your channel strategy and compressing your margins by playing a one-dimensional price game across every surface you have, the only rational response is to move the conversation off price and onto dimensions where you can create value. You can\u2019t do that through a checkout API. You need a system that can negotiate, differentiate, and make the case for your product in ways a product feed never could. The sell-side agent is a defensive response to buyer agent proliferation, not step two in a technology roadmap \u2014 and understanding what you\u2019re defending against is the prerequisite for building it well.<\/p>\n\n

\n\tThat\u2019s what this article is about. Not the sell side, not the protocols \u2014 the buy side. What\u2019s coming through the door, what it looks like, what drives it, and why the answers to those questions should shape every agent investment the sell side makes.<\/p>\n\n\n\tFour agents walk into an airline<\/h2>\n\n

\n\tLet me make this concrete. Imagine you\u2019re an airline \u2014 though the dynamics apply to hotels, retailers, insurers, or anyone selling something with variable pricing and finite inventory. Four different buyer agents arrive at your system within the same hour, all looking for the same route on the same date. They\u2019re all \u201cagents,\u201d but they have almost nothing else in common.<\/p>\n\n

\n\tThe Scripted Searcher<\/strong> is what most people picture when they hear \u201cAI travel agent.\u201d A chatbot with preferences strapped on \u2014 it searches, filters, and presents results, with no real negotiation capability and no contextual awareness beyond what the user typed in. This is where most commercial \u201cAI agents\u201d sit today, barely distinguishable from a competent autocomplete.<\/p>\n\n

\n\tThe Informed Negotiator<\/strong> has the user\u2019s full context \u2014 calendar, budget, loyalty status, trip purpose, date flexibility. It can engage in multi-turn negotiation and make genuine trade-offs: \u201cI\u2019ll take the 06:40 connection if you can offer the upgrade.\u201d The information is loaded at session start, but the negotiation is real. This is the archetype most prototypes are designed against.<\/p>\n\n

\n\tThe Tool-Equipped Agent<\/strong> has everything the Informed Negotiator has, plus live access to external information mid-conversation \u2014 competitor fares, real-time loyalty balances, the airline\u2019s own promotional pricing that the API doesn\u2019t surface. Its advantage isn\u2019t access (a determined human could do the same); it\u2019s unlimited patience applied to a process most humans abandon after ten minutes.<\/p>\n\n

\n\tThe Coalition Agent<\/strong> isn\u2019t acting alone. This archetype opens up three dynamics worth separating: demand aggregation (agents consolidating group demand at machine speed without the humans planning to coordinate), inter-agent intelligence sharing (real-time competitive data from parallel negotiations), and information brokering (pooled completion prices creating a market transparency layer that doesn\u2019t currently exist in consumer travel).<\/p>\n\n\n\tWhen channels stop being separate<\/h2>\n\n

\n\tThe Tool-Equipped Agent deserves particular attention, because it\u2019s the archetype that breaks channel strategy. Airlines, hotels, and retailers have spent decades building channel-specific pricing \u2014 different fares through the website, the GDS, NDC, consolidators. Revenue management depends on this segmentation. A Tool-Equipped Agent treats all channels as a single queryable surface and finds the best option across the lot.<\/p>\n\n

\n\tCritically, this agent has no commitment to completing the transaction where it started the conversation. It might negotiate with your sell-side agent, extract your best offer, and book through a consolidator because the net fare is lower. There\u2019s an assumption in much of the agentic commerce discourse that the agent-to-agent channel will naturally offer the best deals \u2014 because it\u2019s direct, because it cuts out intermediaries. That\u2019s not how distribution works. Consolidators have negotiated net fares below anything the direct channel can offer. A buyer agent that restricts itself to the agent-to-agent channel is, by definition, a worse agent than one that shops everywhere.<\/p>\n\n

\n\tThere\u2019s a subtler problem too. The sell side won\u2019t know where it sits in the buyer agent\u2019s journey. Is this the first exploratory query from an agent early in its search? A follow-up from an enquiry made an hour ago, or a day ago? The start of actual execution? The entire path to purchase becomes obscured, and a seller may never know when they\u2019ve been qualified out of a sale \u2014 never get the chance to provide the one piece of information that would have kept them in contention.<\/p>\n\n\n\tSame engine, different driver<\/h2>\n\n

\n\tThe archetypes describe what buyer agents can do, but capability isn\u2019t the dimension that matters most. Two agents with identical toolkits will behave in fundamentally different ways depending on who\u2019s paying for them.<\/p>\n\n

\n\tThe ad-funded agent<\/strong> is the one most consumers will encounter first, because it\u2019s free. It\u2019s also, structurally, chatbot commerce wearing the clothes of agentic commerce \u2014 the ordering, emphasis, and recommendations shaped by whoever\u2019s paying for placement. An ad-funded Tool-Equipped Agent looks like it\u2019s working for you, but it\u2019s optimising for commission yield: steering toward carriers with higher affiliate payouts, \u201cfailing\u201d to surface cheaper options that would cost its operator revenue.<\/p>\n\n

\n\tIf this sounds theoretical, it shouldn\u2019t. The UK\u2019s motor finance commission scandal and PPI followed exactly this structural pattern \u2014 an intermediary nominally acting for the buyer, actually optimised for the seller\u2019s commission. The regulatory framework for dealing with commission-conflicted intermediaries already exists. It just hasn\u2019t been applied to software yet.<\/p>\n\n

\n\tThe premium user-aligned agent<\/strong> is what you get when the user is the customer \u2014 a subscription service, or a built-in capability from whoever wins the personal-agent market. No commercial relationship with the seller, no reason to reveal its user\u2019s preferences, no channel loyalty. It will open a negotiation with your agent and close through a consolidator without a second thought. It anchors low, waits patiently, and exploits every information asymmetry available. For airlines, hotels, and anyone with perishable inventory, this is the agent that compresses margins.<\/p>\n\n

\n\tThe managed corporate agent<\/strong> sits between the other two \u2014 operated by an employer or TMC, with policy constraints, preferred carriers, and approval workflows. Less price-sensitive, more predictable. The counterpart the sell side secretly wishes they all were.<\/p>\n\n

\n\tThe critical insight: a Tool-Equipped Agent funded by advertising is fundamentally different from one paid for by the user, even with identical capabilities. The first might trade user value for commission and stay in its own channel. The second will use every tool and every channel to optimise for the user\u2019s preference. Same engine, completely different driver.<\/p>\n\n\n\tWhen both sides learn<\/h2>\n\n

\n\tEverything described so far treats the archetypes as static, but these agents will learn \u2014 or more precisely, the infrastructure around them will. A user-aligned agent that negotiates thousands of bookings develops a statistical model of pricing behaviour: which carriers drop fares before departure, which respond to competitive pressure, which concede on bundles more readily than on base fare.<\/p>\n\n

\n\tMore likely than individual agents learning in isolation is the emergence of negotiation intelligence services \u2014 paid tools that pool anonymised transaction data across thousands of agents. An agent doesn\u2019t need to have personally negotiated ten thousand bookings if it can subscribe to a service that has. Of course, data protection constraints are real: GDPR doesn\u2019t care that the primary purpose is commercial intelligence \u2013 but they\u2019re design constraints to be worked with, not a reason not to build something.<\/p>\n\n

\n\tThe sell side will learn too. Over time, sell-side systems may recognise that certain patterns of agent interaction are disproportionately margin-negative \u2014 the sophisticated agents that only show up when a fare is mispriced or competitive pressure creates an unusually good deal. The rational response goes beyond negotiating differently \u2014 it extends to questioning whether to negotiate at all. Tighter identity requirements, less generous offers to unidentified agents, or declining to engage with traffic that pattern-matches to informed counterparts.<\/p>\n\n

\n\tOne plausible outcome: instead of channels collapsing into one transparent market, they deliberately re-fragment. Premium loyalty channels, corporate procurement rails, platform-controlled agent surfaces, public price endpoints \u2014 each with different access levels, pricing, and rules of engagement. The endgame might not be transparency \u2014 it might be a more sophisticated kind of opacity. When both sides are autonomous, learning, and optimising at machine speed, the dynamics start to resemble the algorithmic environments that financial markets have grappled with for decades.<\/p>\n\n\n\tDesigning for the counterpart you can\u2019t predict<\/h2>\n\n

\n\tThe sell side doesn\u2019t get to choose its counterpart. What walks through the door will range from a scripted chatbot to a tool-equipped agent with real-time market intelligence, from a commission-optimised intermediary to a premium personal agent that will methodically extract every pound of value your system will give. You won\u2019t be able to tell, from the outside, which one you\u2019re facing.<\/p>\n\n

\n\tThat means testing against all the archetypes, including the adversarial ones. It means understanding that the buyer-side pressure is what justifies your sell-side investment \u2014 and that an investment shaped by the counterparts you\u2019ll actually face looks very different from one shaped by conference-deck optimism.<\/p>\n\n

\n\tThere is an alternative: don\u2019t participate. Stay with the checkout API, accept that the platform owns the customer relationship, and compete on operational efficiency alone. For some businesses that\u2019s a legitimate strategic choice \u2014 but it\u2019s a one-way door, and the price is every lever except fulfilment.<\/p>\n\n

\n\tThe entire current commercial deployment is chatbot commerce \u2014 platforms calling checkout APIs. The protocols support genuine agent-to-agent interaction, but nobody\u2019s deployed it for commerce yet. The buyer agents that will force that evolution are coming. The organisations that thrive will be the ones that understood the buy side before they built the sell side \u2014 and designed for the counterparts they didn\u2019t choose.<\/p>\n\n\n\t<\/div>\n<\/div>\t<\/div>\n<\/div>\n\n\n\t

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\n\t\t\t\n\t\t\t\tLet\u2019s build what\u2019s next<\/span>\n\tLet\u2019s build what\u2019s next<\/span>\t\t\t<\/h2>\n\n\t\t\t\t\t\t\t

\n\t\t\t\t\tWe work with ambitious organisations to design, build, and scale digital products that solve real problems, improve customer experiences, and deliver measurable impact.\t\t\t\t<\/p>\n\t\t\t\t\t<\/div>\n\n\n\t\t\n\t\n\t\n\t\tCONTACT US\t<\/span>\n\n\t<\/a>\t<\/div>\n<\/div>\t<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"","protected":false},"author":13,"featured_media":989,"template":"","meta":{"es_utils_meta_schema":"","esPageTheme":"default","esPageBackground":"","footnotes":""},"class_list":["post-5026","insight","type-insight","status-publish","has-post-thumbnail","hentry"],"yoast_head":"\nWhy the buy side of agentic commerce is the problem nobody\u2019s solving – Apadmi<\/title>\n<meta name=\"description\" content=\"Buyer agents are coming, and they'll shop every channel at once. 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