{"id":22717,"date":"2026-10-03T02:31:43","date_gmt":"2026-10-03T06:31:43","guid":{"rendered":"https:\/\/www.data-mania.com\/blog\/?p=22717"},"modified":"2026-10-06T23:38:20","modified_gmt":"2026-10-07T03:38:20","slug":"agent-native-product-lessons-hamza-ali","status":"publish","type":"post","link":"https:\/\/www.data-mania.com\/blog\/agent-native-product-lessons-hamza-ali\/","title":{"rendered":"Your Agent Customers Can&#8217;t Pay You Until You Build These 4 APIs (&#038; 6 Other Things I Learned From Hamza Ali)"},"content":{"rendered":"<p><em>Hamza Ali<\/em><em> co-founded <\/em><em>Olostep<\/em><em> in March 2024 on a single bet: agents would become the main user of the web. Today it&#8217;s <\/em><strong><em>seven people<\/em><\/strong><em> and <\/em><strong><em>hundreds of millions of requests a month<\/em><\/strong><em> (billions cumulatively so far). We talked twice. These are the 7 things from those conversations that will change how you build when you have a customer who never opens your website.<\/em><\/p>\n<p>Last weekend, from my desk in Thailand, I went looking for apartment prices in Yerevan, and I decided to run the search through <a href=\"https:\/\/linkly.link\/2ufBw\" target=\"_blank\" rel=\"noopener\">Olostep<\/a>.<\/p>\n<p>I clicked into their answers endpoint and typed in what I wanted. What came back was fast and precise and pointed at a slightly different question than the one in my head, so I brought it up on our call.<\/p>\n<p><a href=\"https:\/\/linkly.link\/2ufEf\" target=\"_blank\" rel=\"noopener\">Hamza<\/a> had the explanation immediately. Answers handles 30-second mini-research. A deep dive like mine belongs on a different endpoint, and they&#8217;ve built one for exactly that.<\/p>\n<p>Then he hit me over the head with this, \u201c<strong>the product isn&#8217;t really meant to be used on the platform\u201d.<\/strong><\/p>\n<p>Here I&#8217;d been poking at a website like a person does. The customer he built it for simply doesn\u2019t do that.<\/p>\n<p>Olostep sells web data infrastructure for AI agents: search, scrape, crawl, and a layer that completes research tasks end to end. Hamza came out of a medicine and surgery degree in Modena, Italy, started companies on the side the whole way through, and moved to San Francisco once they raised.<\/p>\n<p>He built the company with his younger brother. His brother had an offer from Legora while it was still seed stage, helping build the data infrastructure for the agents they were deploying. He was about to take it.<\/p>\n<p>Then a full-ride scholarship from Stanford came through, so he went there, to his parents&#8217; considerable relief. Eighteen months later he dropped out to work on Olostep full time.<\/p>\n<p>They&#8217;re backed by investors in the States and in Europe, and they work mainly with AI-native startups.<\/p>\n<p>I&#8217;ve been writing about AI-native go-to-market for a while now. Hamza is the first founder I&#8217;ve talked to who designed his acquisition, his onboarding, and his pricing around a buyer who has no face (that is, an automated AI agent rather than a human buyer). Here&#8217;s what he&#8217;s learned doing it.<\/p>\n<h2><strong>1. Four APIs let an agent become your customer on its own<\/strong><\/h2>\n<p>I walked into our second call with a question I was sure about. I asked Hamza where he&#8217;d found human assumptions baked into the product that he&#8217;d had to strip out for agents.<\/p>\n<p>He told me that&#8217;s not how it happened.<\/p>\n<p><em>&#8220;We don&#8217;t believe there are a lot of parallels. Like, those are two separate worlds.&#8221;<\/em><\/p>\n<p>They built a second stack alongside the human one, running on the same underlying infrastructure. <strong>The good news is that the pattern is mechanical enough to copy directly.<\/strong> Every single thing a person does by clicking in the dashboard got a matching API:<\/p>\n<ul>\n<li><strong>Sign-up<\/strong> used to be a form, an AWS Cognito pool, and a verification email. Agents can&#8217;t do any of that, so they built a signup API.<\/li>\n<li><strong>Credits<\/strong> used to be a button in the dashboard. Now there&#8217;s an API that buys them.<\/li>\n<li><strong>Your balance<\/strong> used to be a number on a screen. Now there&#8217;s an API that reads it.<\/li>\n<li><strong>Top-ups<\/strong> got their own API too.<\/li>\n<\/ul>\n<p>Three of those four are about money. <strong>An agent that cannot buy credits never becomes a customer<\/strong>, however well your product performs once it gets going.<\/p>\n<p>In Hamza&#8217;s framing, the whole move is this: instead of UI, you move to APIs.<\/p>\n<p>The result showed up in their usage almost immediately. In fact, once those endpoints existed, calls went up, because the agents their customers had already deployed could finally complete the whole loop on their own.<\/p>\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" data-src=\"https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2026\/10\/agent-native-product-lessons-hamza-ali-01.jpg\" alt=\"Infographic titled '4 APIs that let an agent become your customer' mapping four human clicks to agent API calls: a sign-up form becomes a signup API, a buy-credits button becomes a credit purchase API, a balance on a screen becomes a balance API, and a manual top-up becomes a top-up API. Three of the four are about money.\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" style=\"--smush-placeholder-width: 1200px; --smush-placeholder-aspect-ratio: 1200\/1499;\" \/><\/figure>\n<p><strong>Run this: The Click Inventory<\/strong><\/p>\n<ol>\n<li>List every action a customer completes by clicking. Actions, not features.<\/li>\n<li>Ask of each one whether an agent could finish it through an API alone.<\/li>\n<li>Build the click-only ones in the order agents reach them, which puts signup and payment first.<\/li>\n<\/ol>\n<p><em>The test:<\/em> an agent gets from your docs to production with nobody clicking anything.<\/p>\n<p><strong>Key Takeaway:<\/strong> A dashboard feels like your product, and against agent traffic it often represents a dead end. Every click-only action is an endpoint you have not built yet.<\/p>\n<h2><strong>2. Your agent traffic pays you for reliability<\/strong><\/h2>\n<p><em>According to Hamza, &#8220;it&#8217;s harder to build for agents, but it is easier to satisfy them. They don&#8217;t complain that much.&#8221;<\/em><\/p>\n<p>He&#8217;d just finished explaining why he treats the two as separate problems. Human users are easy to build for and brutal to satisfy, because you land squarely in the nuances of psychology, expectation, and taste.<\/p>\n<p>Agents flip that. They&#8217;re hard to build for, and then they either work with your product or they don&#8217;t.<\/p>\n<p><strong>When most people hear &#8220;better product,&#8221; they think &#8220;nicer to use.&#8221;<\/strong> An agent-heavy customer base pays you for whether the API call succeeds.<\/p>\n<p><strong>Key Takeaway:<\/strong> A pretty, user-friendly dashboard feels like a better product, but AI agents only care whether an API request works reliably. Focus your engineering effort on uptime and stability rather than visual UI features that agent customers will never see.<\/p>\n<h2><strong>3. Count your users, then count their agents<\/strong><\/h2>\n<p>Hamza described a typical customer as a team of <strong>20 or 30 people<\/strong> who use the dashboard. That same customer deploys agents in the <strong>thousands<\/strong>.<\/p>\n<p><em>&#8220;There is this disparity between humans and agents, that agents are way more in number.&#8221;<\/em><\/p>\n<p>So the dashboard serves 30 accounts and the API serves thousands of callers, inside one single customer. <strong>That ratio decides where you focus your engineering efforts.<\/strong><\/p>\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" data-src=\"https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2026\/10\/agent-native-product-lessons-hamza-ali-02.jpg\" alt=\"Infographic titled 'Count your users. Then count their agents.' showing that inside one typical Olostep customer account, 20 to 30 humans use the dashboard while thousands of agents call the API, alongside the Agents per Seat steps: count paid seats, count agents running against you, then divide and plot it monthly.\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" style=\"--smush-placeholder-width: 1200px; --smush-placeholder-aspect-ratio: 1200\/960;\" \/><\/figure>\n<p><strong>Run this: Agents per Seat<\/strong><\/p>\n<ol>\n<li>For each of your top accounts, count two numbers: seats they pay for, and agents they run against you.<\/li>\n<li>Divide, then plot it monthly.<\/li>\n<li>Weight your roadmap to the density rather than the seat count.<\/li>\n<\/ol>\n<p><em>The test:<\/em> the trend is your forecast. A climbing ratio against a UI-heavy roadmap means you are building for the smaller half of your own traffic.<\/p>\n<p><strong>Key Takeaway:<\/strong> Standard software metrics track human user seats, but AI-native accounts run hundreds or thousands of automated agents under a single customer contract. Track the ratio of active agents per paid seat so you build for where the actual system traffic and workload live.<\/p>\n<h2><strong>4. Ship the endpoint your customers already stitch together<\/strong><\/h2>\n<p>Olostep&#8217;s answers endpoint exists because Hamza watched customers build it themselves.<\/p>\n<p>They&#8217;d call the search endpoint to get links. Then the scrape endpoint to pull the content. Then they&#8217;d put an LLM on top to reason over whatever came back. That&#8217;s three calls plus a chain of glue code, and different customers were writing that same chain over and over.<\/p>\n<p>So Olostep put those three components together and shipped them as one endpoint. You hand it a question, it does the research, and it returns the answer with its sources. There&#8217;s LLM verification on top to keep the answer grounded in something real.<\/p>\n<p>The use case he described made it concrete for me. Some of Olostep&#8217;s customers onboard large brands, and he named <strong>Nike<\/strong>, <strong>Bank of America<\/strong>, and <strong>MongoDB<\/strong> as examples of clients belonging to those customers.<\/p>\n<p>When a new brand gets onboarded, the customer&#8217;s agent needs two things: a semantic understanding of what that business does, and a list of the most similar companies in a given jurisdiction.<\/p>\n<p>So the agent calls the answers endpoint and asks.<\/p>\n<p>\u2026 What are the companies similar to Nike?<\/p>\n<p>\u2026 What is Nike charging for this product? and<\/p>\n<p>\u2026 What&#8217;s the comparable Adidas price on the market right now?<\/p>\n<p><em>&#8220;It&#8217;s not a full-fledged agent. It&#8217;s an agent that agents use&#8221;\u2014meaning a specialized API endpoint designed specifically to serve other automated systems.<\/em><\/p>\n<p><strong>Run this: The Stitch Test<\/strong><\/p>\n<ol>\n<li>Instrument the sequence of calls your customers make, not just the volume.<\/li>\n<li>Find the same multi-step chain that reappears across accounts that have nothing to do with each other. This independence makes it a signal.<\/li>\n<li>Ship the chain as one thing, and price it against the work it removes.<\/li>\n<\/ol>\n<p><em>The test:<\/em> you have written the same integration advice three times. That is the chain.<\/p>\n<p><strong>Key Takeaway:<\/strong> A feature request seems like the signal to build from, and it often represents what one customer imagines. The chain several unrelated customers already built by hand is what they have proven they will pay for.<\/p>\n<h2><strong>5. Agents discover. People still buy.<\/strong><\/h2>\n<p>I got this wrong the first time, and Hamza corrected me twice before it landed, so let me save you the trip.<\/p>\n<p>After our first call I thought Olostep was running a pipeline where agents found the product, signed up, paid, and grew into enterprise contracts with no person involved anywhere. That would have been a wild story, but the truth turned out to be more useful.<\/p>\n<p><em>&#8220;Right now, agents are not making big purchasing decisions. Those big purchasing decisions are still being made by chief financial officers, by CEOs, by CTOs. And so the agents, for now, for us, it&#8217;s more of a discovery channel.&#8221;<\/em><\/p>\n<p>The real sequence runs like this. An agent finds the product and gets itself onboarded. On the customer&#8217;s side, a flow escalates a good find to a human. That human reaches out, there&#8217;s a call, a Slack channel gets created, and the deal moves from there.<\/p>\n<p>Which brings me to the part I&#8217;d been curious about since our first conversation. <strong>Why did Slack win over email?<\/strong><\/p>\n<p>His answer is about how B2B decisions actually get made. A real deal touches several departments at once, and email can&#8217;t hold that. Threads fragment, context gets lost, and your inbox is full of noise and cold outreach interruptions.<\/p>\n<p>Slack gives them a closed environment that holds one shared context and nothing else.<\/p>\n<p>At the enterprise end, the setup looks like this. One channel holds <strong>15 of the customer&#8217;s engineers<\/strong> and five or six of Olostep&#8217;s, working through the product together. Then a separate channel brings in finance on both sides once it moves to a buying decision. Those channels sit alongside each other, and context moves between them in a way it never does over email.<\/p>\n<p><strong>Run this: Discovery, Not Decision<\/strong><\/p>\n<ol>\n<li>Label every channel by the stage it serves: discovery, qualification, or decision.<\/li>\n<li>Keep a human path to the contract for anything above your self-serve ceiling.<\/li>\n<li>Instrument the handoff, because the escalation happens inside your customer&#8217;s flow where you cannot see it.<\/li>\n<\/ol>\n<p><em>The test:<\/em> ask what stage a channel serves before expecting an agent-driven discovery channel to close enterprise sales.<\/p>\n<p><strong>Run this: The Two-Channel Close<\/strong><\/p>\n<ol>\n<li>Pick the self-serve threshold where usage signals real commercial interest (such as $399\/month).<\/li>\n<li>Open a dedicated shared Slack channel for the technical team and practitioners to collaborate directly with your engineering team.<\/li>\n<li>Set up a parallel channel involving finance and executive buyers when moving to purchase discussions, allowing technical and commercial contexts to stay aligned side by side.<\/li>\n<\/ol>\n<p><em>The test:<\/em> count how many stakeholders across departments share the same context. A single point of contact or siloed email thread means you don&#8217;t have a coordinated deal yet.<\/p>\n<p><strong>Key Takeaway:<\/strong> A channel that produces signups seems ready to produce revenue, but it often just represents the top of the funnel only. Give the buying committee a room, and keep a human on the contract.<\/p>\n<h2><strong>6. When your product replaces payroll, per-request pricing stops working<\/strong><\/h2>\n<p>This is the reframe that changed how I think about Olostep&#8217;s whole category, and Hamza was refreshingly open about the fact that he hasn&#8217;t finished solving it.<\/p>\n<p>They run two pricing layers. The API layer is priced per request, somewhere between <strong>70 and 90 cents per thousand requests<\/strong>, and the more you commit the lower it goes. That&#8217;s standard usage-based pricing, and it works fine.<\/p>\n<p>The agentic search layer is different, because the thing being bought is different. One of their customers does AI procurement for consumer packaged goods. Their agent asks Olostep&#8217;s agent to find every CPG brand in a given US zip code.<\/p>\n<p><strong>That job used to be done by teams of people across the Philippines and India.<\/strong><\/p>\n<p><em>&#8220;You cannot charge per API request, because you are doing work, and you need to charge per outcome.&#8221;<\/em><\/p>\n<p>So on that layer, <strong>Olostep prices against payroll.<\/strong> Here&#8217;s the method, and it&#8217;s the most useful thing in this section:<\/p>\n<ol>\n<li><strong>Find the floor.<\/strong> What does this cost in compute, tokens, and the third-party API calls we have to make?<\/li>\n<li><strong>Find the ceiling.<\/strong> If you had hired teams of people to do this, what would it have cost you?<\/li>\n<li><strong>Price in the middle.<\/strong> Charging the full value of the work removes your customer&#8217;s reason to buy, and charging bare infrastructure cost removes your own financial incentive.<\/li>\n<\/ol>\n<p>Then comes the transaction itself. Their agent sends an invoice to the customer&#8217;s team, the CFO reviews it, there&#8217;s a short negotiation to land on a number that works both ways, and once it settles, the full-scale work kicks off.<\/p>\n<p><em>&#8220;We are entering the era of intelligence&#8230; you cannot price them just by compute, you cannot just price them by API request, because the value you are getting is work, and you need to capture a fraction of that work.&#8221;<\/em><\/p>\n<p>He was clear that this is early and experimental and that he doesn&#8217;t have a definitive answer yet. Fact of the matter is that nobody does yet.<\/p>\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" data-src=\"https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2026\/10\/agent-native-product-lessons-hamza-ali-03.jpg\" alt=\"Infographic titled 'When your product replaces payroll, price the work' comparing Olostep's API layer, priced per request at 70 to 90 cents per 1,000 requests, with its agentic search layer, priced per outcome against payroll, above a floor, ceiling, and middle pricing bar.\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" style=\"--smush-placeholder-width: 1200px; --smush-placeholder-aspect-ratio: 1200\/1499;\" \/><\/figure>\n<p><strong>Run this: Floor, Ceiling, Middle<\/strong><\/p>\n<p>Start with the fork. Tooling gets priced per unit. Labor gets priced per outcome. Unfortunately, most companies in this position keep charging per unit out of habit and hand over the difference.<\/p>\n<ol>\n<li><strong>Floor.<\/strong> What one run costs you in compute, tokens, and the third-party calls you have to make.<\/li>\n<li><strong>Ceiling.<\/strong> What the displaced headcount cost your customer, fully loaded.<\/li>\n<li><strong>Middle.<\/strong> Price between the two, then prove it on a single run before the full job.<\/li>\n<\/ol>\n<p><em>The test:<\/em> you can state both numbers out loud. Teams that cannot name their ceiling are pricing against competitors instead of against the work.<\/p>\n<p><strong>Key Takeaway:<\/strong> Standard API pricing charges per call as if you are selling software tools, but agentic search performs full tasks that replace human labor. When your software replaces workforce payroll, price based on the value of the completed work rather than raw compute requests.<\/p>\n<h2><strong>7. Three layers make your millionth job cheaper than your first<\/strong><\/h2>\n<p>I asked Hamza whether the work compounds. Every job he runs sends his infrastructure out across the web to collect something, so\u2026 does job number one million cost less than job number one?<\/p>\n<p>It does, and he described this by breaking it into three layers.<\/p>\n<p><strong>Layer 1. Economies of scale.<\/strong> More volume buys more infrastructure optimization, and it also buys better commercial terms. The more they commit to their GPU providers, the bigger the discount.<\/p>\n<p><strong>Layer 2. The cache layer.<\/strong> At volume, requests start overlapping. They serve those from cache and skip the fresh job entirely.<\/p>\n<p><strong>Layer 3. Vertical indexes.<\/strong> This one\u2019s quite interesting. They&#8217;re deliberately building something far narrower than Google&#8217;s index.<\/p>\n<p><em>&#8220;We are not building a general index like Google. What we are doing is building very specialized, critical indexes for our customers, because they work in some specific industry.&#8221;<\/em><\/p>\n<p>Their customers in the legal industry only want their agents searching that vertical. Other customers don&#8217;t care about news, and they definitely don&#8217;t care about recipes. So Olostep builds narrow, deep indexes, one per vertical.<\/p>\n<p><em>&#8220;One request will cost X. The <\/em><strong><em>millionth request<\/em><\/strong><em> would cost significantly less, because the index is already built. You don&#8217;t need to build it from scratch every time.&#8221;<\/em><\/p>\n<p>When I put it to him that this reads like a unique value proposition, and not just an infrastructure detail, he agreed it is.<\/p>\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" data-src=\"https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2026\/10\/agent-native-product-lessons-hamza-ali-04.jpg\" alt=\"Infographic titled '3 layers that make your millionth job cheaper' showing economies of scale, a cache layer, and vertical indexes, with the cost per job falling from job 1 to job 1,000,000 as the layers stack.\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" style=\"--smush-placeholder-width: 1200px; --smush-placeholder-aspect-ratio: 1200\/960;\" \/><\/figure>\n<p><strong>Run this: The Compounding Audit<\/strong><\/p>\n<p>Put a number against each of the three, in one sentence each.<\/p>\n<ol>\n<li><strong>Purchasing leverage.<\/strong> Does volume buy you better terms?<\/li>\n<li><strong>Redundancy.<\/strong> Can you serve overlapping work from cache?<\/li>\n<li><strong>Accumulated asset.<\/strong> Does the index you build make the next job cheaper?<\/li>\n<\/ol>\n<p><em>The test:<\/em> if none of the three apply, volume will not save your margins.<\/p>\n<p><strong>Key Takeaway:<\/strong> Building a focused, specialized search index for a specific industry allows you to answer repetitive agent requests from saved data rather than re-scraping the web every time. Over time, cached vertical data creates a permanent cost advantage that competitors cannot match.<\/p>\n<h2><strong>He&#8217;s building for machines, yet he closes deals face-to-face<\/strong><\/h2>\n<p>I want to end on this\u2026<\/p>\n<p>Olostep is embedded inside other companies&#8217; products. <a href=\"https:\/\/www.gumloop.com\/\" target=\"_blank\" rel=\"noopener\">Gumloop<\/a> signed while it was still in its YC batch and now runs Olostep as its default web node, and a dozen or so more logos on the homepage sit in similar positions.<\/p>\n<p>So I asked how you win that kind of placement, because embedded distribution is the most valuable real estate there is. I expected an answer about docs, or developer experience, or a partnerships motion.<\/p>\n<p>Here&#8217;s what he told me they tried first: LinkedIn messages, case studies from other customers, email. All of it produced polite interest and then silence.<\/p>\n<p><em>&#8220;We got some &#8216;we are interested,&#8217; but things never moved. We got stuck there.&#8221;<\/em><\/p>\n<p>What actually worked was getting on a plane.<\/p>\n<p><em>&#8220;We have never lost a deal where we actually presented ourselves in person.&#8221;<\/em><\/p>\n<p>They fly to the customer. They show up at the office, sometimes unannounced, with two packets of Dunkin&#8217; Donuts. I asked whether Krispy Kreme would also work. He thinks it probably would \ud83d\ude09<\/p>\n<p>And the way they figured it out is the part worth keeping. In the early days they sold to friends inside their own social circles, so deals closed in days and they had no idea which part of that was doing the work.<\/p>\n<p>As they scaled, cycles stretched from days to weeks to months. When they went looking for the missing variable, they discovered that it was the human connection they&#8217;d had by default at the start but lost as they grew. Showing up in person put it back, and the pace picked up again.<\/p>\n<p>So the company building infrastructure for a customer with no face wins its biggest deals face to face.<\/p>\n<p>I asked Hamza at the end whether there was anything I hadn&#8217;t asked about, and he went straight there:<\/p>\n<p><em>&#8220;Even if we are entering an era of agents, an era of machines doing work on our behalf, I think fundamentally companies and societies are still made by a bunch of humans who share the same values and are working towards a common goal&#8230; Your company or your society can only be as good as the people you have.&#8221;<\/em><\/p>\n<p>He wants the symbiosis in the middle, and he pushes back on the two fantasies at either end: one person running a thousand agents, or a company that refuses to touch any of this. Coming from the founder whose entire product thesis is that machines are the web&#8217;s next user, I found that refreshing.<\/p>\n<p>If you&#8217;re building anything agents are starting to touch, Hamza asked for something specific: sign up, get into their <a href=\"https:\/\/linkly.link\/2ufBx\" target=\"_blank\" rel=\"noopener\">Slack<\/a>, and tell them what you or the agent you&#8217;re deploying is trying to do on the web. That&#8217;s a useful offer from someone this far ahead on the problem.<\/p>\n<p>If something in here doesn&#8217;t map onto your setup, hit reply and tell me where it breaks. I read every one of these, and the sharpest questions tend to become the next brief.<\/p>\n<p>Warmly,<\/p>\n<p>Lillian Pierson<\/p>\n<p>P.S. The thing I keep coming back to is my own Yerevan search at the start of this. I went to a website and typed a question into a box, which is what a person does. The customer Olostep built for would have read the docs, called the right endpoint, and had the answer before I finished reading the homepage. <strong>If you&#8217;re wondering whether your own product is ready for agents, there&#8217;s your test: if an agent ever has to rely on a human to click something, sign up, or verify, the product isn&#8217;t fully agent-ready.<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Olostep co-founder Hamza Ali on the 4 APIs that let AI agents pay you, pricing work that replaces payroll, and 5 more lessons from building for agents.<\/p>\n","protected":false},"author":1,"featured_media":22714,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_wp_convertkit_post_meta":{"form":"-1","landing_page":"0","tag":"0","restrict_content":"0"},"footnotes":"","_links_to":"","_links_to_target":""},"categories":[830,853,856,582],"tags":[],"class_list":["post-22717","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-convergence-feature","category-convergence-guest","category-pricing-monetization","category-startups"],"_links":{"self":[{"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/posts\/22717","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/comments?post=22717"}],"version-history":[{"count":7,"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/posts\/22717\/revisions"}],"predecessor-version":[{"id":22730,"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/posts\/22717\/revisions\/22730"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/media\/22714"}],"wp:attachment":[{"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/media?parent=22717"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/categories?post=22717"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/tags?post=22717"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}