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<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="preload" href="/_next/static/media/345b029bff63acdd-s.p.14zxo1jbez.lc.woff2" as="font" crossorigin="" type="font/woff2"/><link rel="preload" href="/_next/static/media/8e6fa89aa22d24ec-s.p.0uvzar8hswo3p.woff2" as="font" crossorigin="" type="font/woff2"/><link rel="preload" href="/_next/static/media/ce62453a442c7f35-s.p.0333ktddfbsxy.woff2" as="font" crossorigin="" type="font/woff2"/><link rel="preload" href="/_next/static/media/e2334d715941921e-s.p.12skym0rqknxy.woff2" as="font" crossorigin="" type="font/woff2"/><link rel="preload" as="image" imageSrcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fee7172fc6d57df70b4b2dd303f6a58fc87e24e93-3840x2160.png&amp;w=384&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fee7172fc6d57df70b4b2dd303f6a58fc87e24e93-3840x2160.png&amp;w=640&amp;q=75 2x"/><link rel="stylesheet" href="/_next/static/chunks/0-a~7ljgpwyij.css" data-precedence="next"/><link rel="stylesheet" href="/_next/static/chunks/0k9no2apwmz~n.css" data-precedence="next"/><link rel="stylesheet" href="/_next/static/chunks/0nuksys438q2t.css" data-precedence="next"/><link rel="preload" as="script" fetchPriority="low" href="/_next/static/chunks/05gogik_6g8vk.js"/><script src="/_next/static/chunks/008dsykc_h8bq.js" async=""></script><script src="/_next/static/chunks/0i~~aj0ee08p-.js" async=""></script><script src="/_next/static/chunks/0qw_yv-45a3f4.js" async=""></script><script src="/_next/static/chunks/0._rkt7m1wq9e.js" async=""></script><script src="/_next/static/chunks/turbopack-16vnx6ub8-z5v.js" async=""></script><script src="/_next/static/chunks/14_mcc2-.c1ee.js" async=""></script><script src="/_next/static/chunks/019vi3l~x0zoc.js" async=""></script><script src="/_next/static/chunks/12~zlyzq33k~1.js" async=""></script><script src="/_next/static/chunks/025lo2k4b20~m.js" async=""></script><script src="/_next/static/chunks/0ku~nn_l8-fol.js" async=""></script><script src="/_next/static/chunks/039u5_5hlemld.js" async=""></script><script src="/_next/static/chunks/189hrhe.fk4af.js" async=""></script><script src="/_next/static/chunks/0oz-qxk30w~qw.js" async=""></script><script src="/_next/static/chunks/0vx_phl8cla2s.js" async=""></script><script src="/_next/static/chunks/0xywjwd04osrr.js" async=""></script><script src="/_next/static/chunks/0t1~yeqvn891r.js" async=""></script><script src="/_next/static/chunks/0ks4m351.jbv2.js" async=""></script><script src="/_next/static/chunks/01gy5f4286e6a.js" async=""></script><script src="/_next/static/chunks/0h3ejemwt0u_c.js" async=""></script><link rel="preload" href="https://cdn-cookieyes.com/client_data/497224d8995aa8565cbc3037/script.js" as="script"/><link rel="preload" href="/hny.min.js" as="script"/><link rel="preload" href="https://stats.honeycomb.io/js/pa-DJvf3AtvU5wDjKkEU-JVs.js" as="script"/><link rel="preload" href="https://www.googletagmanager.com/gtag/js?id=G-DJM5ZXPGB0" as="script"/><link rel="preload" href="https://www.googletagmanager.com/gtm.js?id=GTM-KH7NL6K" as="script"/><link rel="preload" href="https://cdn.jsdelivr.net/npm/hockeystack@latest/hockeystack.min.js" as="script"/><link rel="preconnect" href="https://cdn-cookieyes.com"/><link rel="dns-prefetch" href="https://cdn-cookieyes.com"/><meta name="next-size-adjust" content=""/><title>What Comes After Observability?</title><meta name="description" content="A year ago, I predicted ways in which AI was about to fundamentally change observability as we knew it. Here&#x27;s what actually happened."/><link rel="canonical" href="https://www.honeycomb.io/blog/what-comes-after-observability"/><meta property="og:title" content="What Comes After Observability?"/><meta property="og:description" content="A year ago, I wrote “It’s the End of Observability (and I Feel Fine). ” The upshot of that post was that AI was about to fundamentally change the way we approach systems design and operation in the future."/><meta property="og:site_name" content="Honeycomb"/><meta property="og:locale" content="en_US"/><meta property="og:image" content="https://cdn.sanity.io/images/927dxq0h/production/e074790a74ac795de3c41ddaceb84ff80f8393e3-3840x2160.png?rect=0,77,3840,2006&amp;w=1200&amp;h=627&amp;fit=crop"/><meta property="og:image:width" content="1200"/><meta property="og:image:height" content="627"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2026-07-20T15:00:00.000Z"/><meta property="article:modified_time" content="2026-07-20T15:00:00.000Z"/><meta name="twitter:card" content="summary_large_image"/><meta name="twitter:title" content="What Comes After Observability?"/><meta name="twitter:description" content="A year ago, I wrote “It’s the End of Observability (and I Feel Fine). ” The upshot of that post was that AI was about to fundamentally change the way we approach systems design and operation in the future."/><meta name="twitter:image" content="https://cdn.sanity.io/images/927dxq0h/production/e074790a74ac795de3c41ddaceb84ff80f8393e3-3840x2160.png?rect=0,77,3840,2006&amp;w=1200&amp;h=627&amp;fit=crop"/><meta name="twitter:image:width" content="1200"/><meta name="twitter:image:height" content="627"/><link rel="icon" href="/favicon.ico?favicon.07o~09ebmt8eg.ico" sizes="48x48" type="image/x-icon"/><link rel="icon" href="/favicon-16x16.png" sizes="16x16" type="image/png"/><link rel="icon" href="/favicon-32x32.png" sizes="32x32" 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lg:pointer-events-none lg:block lg:py-10">Learn <svg width="11" height="12" viewBox="0 0 11 12" fill="none" xmlns="http://www.w3.org/2000/svg" class="lg:hidden"><path d="M5 1L10 6L5 11" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"></path></svg></button><div class="jsx-62ee9791d3e4bddb menu fixed left-0 z-50 hidden w-full bg-[linear-gradient(to_right,_#f6f6f6_50%,_white_50%)] lg:top-[var(--navBottom,104px)] lg:group-focus-within:block lg:group-hover:block"><div class="jsx-62ee9791d3e4bddb container flex gap-12 xl:max-w-[1180px]"><div class="jsx-62ee9791d3e4bddb max-w-[170px] pt-12 pb-16"><h4 class="jsx-62ee9791d3e4bddb font-roboto mb-2 text-2xl font-bold">Observability Engineering</h4><p class="jsx-62ee9791d3e4bddb mb-6 text-sm tracking-wide"> <!-- -->Start your journey with the definitive guide to observability. 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Honeycomb","isPartOf":{"@id":"https://www.honeycomb.io/#website"},"inLanguage":"en-US","primaryImageOfPage":{"@id":"https://www.honeycomb.io/blog/what-comes-after-observability#primaryimage"},"image":{"@id":"https://www.honeycomb.io/blog/what-comes-after-observability#primaryimage"},"thumbnailUrl":"https://cdn.sanity.io/images/927dxq0h/production/e074790a74ac795de3c41ddaceb84ff80f8393e3-3840x2160.png","datePublished":"2026-07-20T15:00:00+00:00","dateModified":"2026-08-12T17:09:03+00:00","description":"A year ago, I predicted ways in which AI was about to fundamentally change observability as we knew it. 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Here&#x27;s what we&#x27;ve seen happen since—both at Honeycomb and with our customers—and what we&#x27;re building for the future.</p></div></div><div class="flex w-full flex-col gap-3"><div class="flex w-full flex-col items-center justify-between gap-5 md:flex-row"><div class="flex flex-row items-center px-5 md:px-0"><div class="flex items-center gap-[14px]"><img alt="Austin Parker" loading="lazy" width="80" height="80" decoding="async" data-nimg="1" class="bg-hc-gray-50 size-10 rounded-full" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2F2a1050e83bac34eea79bd9f2d5bf7c3fc106550f-600x600.jpg%3Fw%3D80%26h%3D80&amp;w=96&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2F2a1050e83bac34eea79bd9f2d5bf7c3fc106550f-600x600.jpg%3Fw%3D80%26h%3D80&amp;w=256&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2F2a1050e83bac34eea79bd9f2d5bf7c3fc106550f-600x600.jpg%3Fw%3D80%26h%3D80&amp;w=256&amp;q=75"/><p class="text-hc-slate text-base"><span>By: </span><a class="text-hc-cobalt underline hover:no-underline" href="/author/austin">Austin Parker</a></p></div><span class="text-hc-slate font-roboto text-base font-medium"><span class="ml-1"> | </span><span>July 20, 2026</span></span></div><div class="bg-hc-gray-100 h-0.5 w-full md:hidden"></div><div class="flex flex-wrap gap-3 px-5 md:px-0"><a href="/blog/category/observability"><div class="font-roboto focus:ring-ring inline-flex items-center rounded-lg font-medium transition-colors focus:ring-2 focus:ring-offset-2 focus:outline-none px-2 py-1 text-xs leading-normal bg-hc-gray-200 text-hc-slate">Observability</div></a><a href="/blog/category/ai-llms"><div class="font-roboto focus:ring-ring inline-flex items-center rounded-lg font-medium transition-colors focus:ring-2 focus:ring-offset-2 focus:outline-none px-2 py-1 text-xs leading-normal bg-hc-gray-200 text-hc-slate">AI &amp; LLMs</div></a></div></div><div class="bg-hc-gray-100 hidden h-0.5 w-full md:block"></div></div></header><div class="m-auto w-full max-w-[1116px] justify-between lg:flex lg:gap-[80px] lg:py-10"><div class="h-fit lg:sticky lg:top-28 lg:w-[280px]"><div class="hidden overflow-hidden rounded-[22px] bg-gray-50 lg:block"><img alt="The Second Edition of Observability Engineering Is Here" width="280" height="184" decoding="async" data-nimg="1" class="h-[184px] object-cover" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fee7172fc6d57df70b4b2dd303f6a58fc87e24e93-3840x2160.png&amp;w=384&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fee7172fc6d57df70b4b2dd303f6a58fc87e24e93-3840x2160.png&amp;w=640&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fee7172fc6d57df70b4b2dd303f6a58fc87e24e93-3840x2160.png&amp;w=640&amp;q=75"/><div class="p-[30px] pt-5"><div class="mb-3 flex items-center gap-4"><div class="font-roboto focus:ring-ring inline-flex items-center rounded-lg font-medium transition-colors focus:ring-2 focus:ring-offset-2 focus:outline-none px-2 py-1 text-xs leading-normal bg-hc-gray-200 text-hc-slate">Blog</div><span class="h-3.5 border-r border-r-black/30"></span><span class="font-medium font-roboto text-hc-slate/70 text-xs"><span>June 18, 2026</span></span></div><div class="space-y-4"><h4 class="font-roboto text-sm font-medium md:text-base">The Second Edition of Observability Engineering Is Here</h4><p>The second edition of Observability Engineering is available for download on our website.</p><a target="_self" _type="link" class="text-hc-slate hover:text-hc-cobalt group/arrow-link items-center gap-2 font-roboto focus-visible:ring-ring cursor-pointer rounded-sm text-center font-bold tracking-[1.4px] uppercase transition-[color,background-color] [transition-duration:300ms,500ms] focus-visible:ring-1 focus-visible:outline-none disabled:pointer-events-none disabled:opacity-50 flex !p-0 px-4 py-[13px] text-[14px] leading-[14px] [&amp;_span]:text-hc-cobalt [&amp;_svg]:text-hc-cobalt" href="/blog/the-second-edition-is-here"><span class="flex items-center justify-center gap-2.5">Download</span><svg width="11" height="12" viewBox="0 0 11 12" fill="none" xmlns="http://www.w3.org/2000/svg" class="transition-transform group-hover/arrow-link:translate-x-2"><path d="M5 1L10 6L5 11" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"></path></svg></a></div></div></div></div><div class="min-w-0 flex-1" style="content-visibility:auto;contain-intrinsic-size:auto 1500px"><img alt="What Comes After Observability?" fetchPriority="low" loading="lazy" width="3840" height="2160" decoding="async" data-nimg="1" class="my-5 block h-auto w-full max-w-full rounded-4xl object-center object-contain" style="color:transparent" sizes="100vw" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fe074790a74ac795de3c41ddaceb84ff80f8393e3-3840x2160.png&amp;w=640&amp;q=75 640w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fe074790a74ac795de3c41ddaceb84ff80f8393e3-3840x2160.png&amp;w=750&amp;q=75 750w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fe074790a74ac795de3c41ddaceb84ff80f8393e3-3840x2160.png&amp;w=828&amp;q=75 828w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fe074790a74ac795de3c41ddaceb84ff80f8393e3-3840x2160.png&amp;w=1080&amp;q=75 1080w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fe074790a74ac795de3c41ddaceb84ff80f8393e3-3840x2160.png&amp;w=1200&amp;q=75 1200w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fe074790a74ac795de3c41ddaceb84ff80f8393e3-3840x2160.png&amp;w=1920&amp;q=75 1920w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fe074790a74ac795de3c41ddaceb84ff80f8393e3-3840x2160.png&amp;w=2048&amp;q=75 2048w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fe074790a74ac795de3c41ddaceb84ff80f8393e3-3840x2160.png&amp;w=3840&amp;q=75 3840w" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fe074790a74ac795de3c41ddaceb84ff80f8393e3-3840x2160.png&amp;w=3840&amp;q=75"/><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">A year ago, I wrote <a class="not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline" href="/blog/its-the-end-of-observability-as-we-know-it-and-i-feel-fine">“It’s the End of Observability (and I Feel Fine).”</a> The upshot of that post was that AI was about to fundamentally change the way we approach systems design and operation in the future. In the grand tradition, I’d like to revisit my claims from then and see how my predictions panned out.</p><h2 id="claim:-agents-can-zero-shot-investigations-for-less-than-a-dollar" class="text-h3 text-hc-slate not-prose mt-6 xl:scroll-mt-24">Claim: Agents can zero-shot investigations for less than a dollar</h2><blockquote class="not-prose font-roboto border-hc-gray-200 text-hc-slate mx-2 mt-10 mb-10 border-l-2 pl-4 text-base leading-normal font-normal tracking-normal lg:mt-16 lg:mr-11 lg:mb-16 lg:ml-11 lg:pl-[31px]"><em>I asked the agent the same question we’d ask you in a demo, and the agent figured it out with no additional prompts, training, or guidance…and it did it for sixty cents.</em></blockquote><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">I pointed out in my original blog that the way I evaluated our MCP server was to feed it a prompt based on the same demo that we give to people—e.g., here’s this weird latency spike, investigate it, tell me why it happened. At the time, that demo took eight tool calls and about $0.60 of inference. A year later, we process about 2 million agent-initiated query runs through Honeycomb every month!</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">What’s really interesting, though, is that we’ve seen agents start to accomplish significantly longer-horizon tasks. Frontier models—like Sonnet or Fable 5—are increasingly agentic and capable. In the same agent session, we’ve seen tool calls double on average between February and June of this year. Our original demo of eight queries looks downright quaint.</p><div data-testid="article-banner-resource"><section class="col-span-full py-10 container md:py-12"><div class="relative flex min-h-[300px] flex-col justify-between overflow-hidden rounded-2xl bg-sky-50 p-5 lg:min-h-[186px] lg:flex-row lg:items-stretch lg:rounded-[10px] lg:px-10 lg:py-8"><div class="content-center"><h1 _type="title" class="text-pretty font-poppins mt-0 mb-4 font-semibold [&amp;_.title-heading-4]:text-2xl lg:[&amp;_.title-heading-4]:text-4xl"><span class="title-heading-3 text-3xl lg:text-4xl">Watch Austin, on demand</span></h1><div class="font-roboto mb-0 text-base"><p>Watch Austin Parker and the other authors </p><p>of Observability Engineering</p><p>discuss what&#x27;s changed in the AI era.</p></div></div><div class="z-10 lg:absolute lg:right-10 lg:bottom-8"><div class="flex w-full flex-col justify-center gap-2 md:flex-row"><a target="_self" _type="link" class="font-roboto focus-visible:ring-ring inline-block cursor-pointer rounded-sm text-center font-bold tracking-[1.4px] uppercase transition-[color,background-color] [transition-duration:300ms,500ms] focus-visible:ring-1 focus-visible:outline-none disabled:pointer-events-none disabled:opacity-50 border-hc-cobalt bg-hc-cobalt hover:bg-hc-pacific hover:border-hc-pacific border-2 border-solid text-white px-4 py-[13px] text-[14px] leading-[14px] no-underline" href="/resources/webinars/ama-new-engineering-realities-observability-engineering-authors"><span class="flex items-center justify-center gap-2.5">Watch Now</span></a></div></div><div class="absolute top-1/2 -left-[60px] h-[537px] w-[636px] -translate-y-1/2 opacity-10 md:left-[44%] lg:h-[1418px] lg:w-[1680px]"><svg xmlns="http://www.w3.org/2000/svg" fill="none" style="width:100%;height:auto" viewBox="0 0 1149 969"><path fill="var(--color-hc-sky-200)" d="M858.898 0H291.205a5 5 0 0 0-4.299 2.447L1.519 483.023a5 5 0 0 0 .005 5.114l285.38 478.424a5.001 5.001 0 0 0 4.295 2.439h567.706a4.998 4.998 0 0 0 4.298-2.446L1147.49 488.13a5.01 5.01 0 0 0 0-5.1L863.202 2.454A5 5 0 0 0 858.898 0Z"></path></svg></div><div class="absolute top-1/2 left-[70px] h-[1418px] w-[1680px] -translate-y-1/2 opacity-10 md:left-[61%] lg:h-[537px] lg:w-[636px]"><svg xmlns="http://www.w3.org/2000/svg" fill="none" style="width:100%;height:auto" viewBox="0 0 1149 969"><path fill="var(--color-hc-sky-300)" d="M858.898 0H291.205a5 5 0 0 0-4.299 2.447L1.519 483.023a5 5 0 0 0 .005 5.114l285.38 478.424a5.001 5.001 0 0 0 4.295 2.439h567.706a4.998 4.998 0 0 0 4.298-2.446L1147.49 488.13a5.01 5.01 0 0 0 0-5.1L863.202 2.454A5 5 0 0 0 858.898 0Z"></path></svg></div></div></section></div><h2 id="claim:-humans-stay-in-the-loop" class="text-h3 text-hc-slate not-prose mt-6 xl:scroll-mt-24">Claim: Humans stay in the loop</h2><blockquote class="not-prose font-roboto border-hc-gray-200 text-hc-slate mx-2 mt-10 mb-10 border-l-2 pl-4 text-base leading-normal font-normal tracking-normal lg:mt-16 lg:mr-11 lg:mb-16 lg:ml-11 lg:pl-[31px]"><em>I’m not gonna sit here and say this destroys the idea of humans being involved in the process, though. I don’t think that’s true. The rise of the cloud didn’t destroy the idea of IT. The existence of Rails doesn’t mean we don’t need server programmers. Productivity increases expand the map. There’ll be more software, of all shapes and sizes. We’re going to need more of everything.</em></blockquote><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">What’s really interesting is that <em>human-initiated queries haven’t shrunk</em>. Agents aren’t taking investigations away from humans, they’re <em>accentuating</em> them. People still use the UI, people still check their boards, but they’re now able to do more than they could before by leveraging AI. We see this a lot in terms of edits—while people <em>are</em> using agents to build boards and update SLOs, the overwhelming majority of tool calls are to just run queries.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">In my blog, I claimed that productivity increases weren’t going to diminish the impact or effect of people, and that’s what the data shows. The map is not the territory, but the map has grown significantly thanks to agents.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">This doesn’t mean that all of these agents are being driven by a human, though. We’re seeing an increasing amount of headless agents using our MCP; it’s actually the fastest-growing segment.</p><h2 id="claim:-it’s-only-getting-cheaper-to-do-this" class="text-h3 text-hc-slate not-prose mt-6 xl:scroll-mt-24">Claim: It’s only getting cheaper to do this</h2><blockquote class="not-prose font-roboto border-hc-gray-200 text-hc-slate mx-2 mt-10 mb-10 border-l-2 pl-4 text-base leading-normal font-normal tracking-normal lg:mt-16 lg:mr-11 lg:mb-16 lg:ml-11 lg:pl-[31px]"><em>Inference costs are only going down…If your product’s value proposition is nice graphs and easy instrumentation, you are cooked. An LLM commoditizes the analysis piece, OpenTelemetry commoditizes the instrumentation piece.</em></blockquote><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">In my original post, I focused on the idea of inference costs going down. I think that’s, broadly, still true (even if a lot of people are getting sticker shock as they transition from all-you-can-eat to API pricing for development workloads). That said, I’m writing this post on a laptop that effortlessly runs Google’s Gemma4 model, and it’s perfectly capable of using our MCP. I think, long-run, the “cost of AI” is going to keep going down at a fixed level of capability.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">What I didn’t call, though, is that the robots are actually surprisingly efficient when it comes to <em>using</em> Honeycomb! On average, agent queries cost about half as much to serve as human ones. There’s a pretty easy explanation for this: the agents are able to much more accurately figure out what to search for, especially if they’re running with access to your code. They know exactly what to look at, and they don’t need to spend as much time searching across an entire environment to orient themselves.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">Interestingly enough, we also notice that agents—on average—tend to run queries over a 50% shorter time range than humans. Where this gets really interesting is how <em>different</em> the agent runs tend to be. Custom agents tend to look at smaller time windows, while human-driven ones tend to look across longer periods of time. My hypothesis is that those custom agents are probably more task-oriented (“Here’s an error, look around this time for traces”) vs. more exploratory work being done in the development loop.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">The headline number, though? On average, an agent-initiated query fans out to 3.3x fewer lambdas, scans 1.8x fewer bytes, and burns 2.2x less compute. What does this mean for us? Well, from March to June we’ve grown 4x in query volume while only increasing cost by 23%, leading to a 72% reduction in unit costs for agent-initiated queries. I’ll take that!</p><img data-testid="article-image" alt="Average per-query run of human-initiated queries compared to agent-initiated queries." fetchPriority="low" loading="lazy" width="3840" height="2160" decoding="async" data-nimg="1" class="not-prose mt-6 max-h-[451px] rounded-[22px] block h-auto w-full" style="color:transparent" sizes="100vw" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Ff4a4555365d1b22f17ee5bf3c66bc36905701798-3840x2160.png&amp;w=640&amp;q=75 640w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Ff4a4555365d1b22f17ee5bf3c66bc36905701798-3840x2160.png&amp;w=750&amp;q=75 750w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Ff4a4555365d1b22f17ee5bf3c66bc36905701798-3840x2160.png&amp;w=828&amp;q=75 828w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Ff4a4555365d1b22f17ee5bf3c66bc36905701798-3840x2160.png&amp;w=1080&amp;q=75 1080w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Ff4a4555365d1b22f17ee5bf3c66bc36905701798-3840x2160.png&amp;w=1200&amp;q=75 1200w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Ff4a4555365d1b22f17ee5bf3c66bc36905701798-3840x2160.png&amp;w=1920&amp;q=75 1920w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Ff4a4555365d1b22f17ee5bf3c66bc36905701798-3840x2160.png&amp;w=2048&amp;q=75 2048w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Ff4a4555365d1b22f17ee5bf3c66bc36905701798-3840x2160.png&amp;w=3840&amp;q=75 3840w" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Ff4a4555365d1b22f17ee5bf3c66bc36905701798-3840x2160.png&amp;w=3840&amp;q=75"/><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24"></p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">An interesting coda to this, though, is that agents are <em>significantly worse</em> for caching. While we don’t see significant amounts of caching for interactive queries in general, we see almost <em>none</em> for agent-driven queries. This is mostly because agents don’t need to ask the same question twice. I’ve also personally observed that agents prefer to re-run queries rather than reuse existing ones, which is interesting and probably deserves more attention.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">That said, we’ve seen pretty <a class="not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline" href="/blog/30-70-prs-day-how-we-managed-not-wreck-systems">staggering levels of adoption</a>, especially in the enterprise. One of our larger customers read 62 PiB of data in one month&#x27;s time <em>exclusively</em> through agents, mostly through off-the-shelf tools like Claude Code.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">There’s a downside to this as well, though: a lot of those queries wind up being kinda slow, because the agents aren’t chewing through nicely structured data, they’re grepping across unstructured body fields. Structured, high-cardinality, high-dimensionality data is no longer a nice-to-have for humans, <strong>it is a requirement for making agent investigations affordable</strong>. All of that data that the agent has to chew through in order to find the needle in the haystack? That’s tokens coming out of your pocket. Do you want to pay Fable rates to look through gigabytes of crappy logs?</p><h2 id="claim:-fast-feedback-loops-are-all-that-matter" class="text-h3 text-hc-slate not-prose mt-6 xl:scroll-mt-24">Claim: Fast feedback loops are all that matter</h2><blockquote class="not-prose font-roboto border-hc-gray-200 text-hc-slate mx-2 mt-10 mb-10 border-l-2 pl-4 text-base leading-normal font-normal tracking-normal lg:mt-16 lg:mr-11 lg:mb-16 lg:ml-11 lg:pl-[31px]"><em>I’m gonna put a marker out there: the only thing that really matters is fast, tight feedback loops at every stage of development and operations. AI thrives on speed—it’ll outrun you every time.</em></blockquote><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">Just go ask Claude to summarize the current state of the “Loops” discourse and you’ll see that the rest of the AI thought leadership industry is talking about something you read here a year ago.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">Anyway, there’s no prize for being first, so I instead want to share a story from one of our customers. Guy writes in to tell us how much he loves MCP and how he’s using it. He’s hooked up everything to agents that talk to Honeycomb. When a customer reports an issue or someone files a bug, the agent goes out to verify it using production telemetry. When an SLO burns, agent goes out, looks into it. Agent takes that investigation, hands it off to another agent, which goes out and writes a fix, makes a PR. Yet another agent reviews the PR and pings a human for final review and merge. PR gets deployed, goes into prod, wakes up that original agent to look at production telemetry to see if the problem was fixed. If it wasn’t, enter the loop again.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">That’s the kind of tool that we’re building at Honeycomb today, yes. But what we’re building for tomorrow is so much more than that. I don’t want us to build <em>just</em> a really fast column store (although a really fast column store is cool). I want to make it easy for everyone to have these fast feedback loops. I want you to be able to understand what your agents are doing with your code, and I want to make it easy for you—and your agents—to learn about production and turn those learnings into knowledge.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">If you’ve had the chance to check out <a class="not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline" href="/platform/canvas">Canvas</a>, and our Canvas Agent, you’ve seen the first cut of this (and if you haven’t, you should check it out; it’s free!). This is just the first chapter of what we’re building, though. If you&#x27;re interested in learning more about how we see AI changing observability and what we&#x27;re working on here at Honeycomb, join me and the other authors of the Observability Engineering book <a class="not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline" href="/resources/webinars/ama-new-engineering-realities-observability-engineering-authors">in our on-demand AMA</a>.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">P.S. <a class="not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline" href="/careers">We’re hiring</a> if you wanna come work on agents with us.</p></div></div><aside id="relatedArticles" class="col-span-full py-12 lg:py-24"><div class="z-20 flex flex-col items-center lg:w-1/2 lg:gap-8 mx-auto mb-0 gap-8 text-center lg:mb-0"><h1 _type="title" class="font-medium text-pretty font-poppins text-center text-4xl lg:text-5xl"><span class="title-hero-heading text-4xl tracking-tighter lg:text-6xl"></span></h1></div><div class="gap-4 py-10 lg:py-16 xl:flex xl:gap-6"><div class="full flex flex-col gap-4 md:flex-row lg:flex-col xl:w-[831px] xl:gap-6"><a class="flex-1 lg:h-[245px]" href="/resources/podcasts/ep-93-adaptive-sampling-strategies-with-mike-goldsmith"><div class="bg-hc-gray-50 flex h-full flex-col gap-6 rounded-4xl p-2.5 lg:flex-row"><div class="relative h-[172px] w-auto md:h-[177px] lg:h-[225px] lg:w-[396px] xl:flex-1"><img alt="O11yCast podcast logo: a dark background with a radiating orange, blue, green, and yellow circular pattern above the white and orange text &#x27;011Y_Cast(((&#x27; and &#x27;Everything Observability&#x27;." loading="lazy" decoding="async" data-nimg="fill" class="aspect-video rounded-4xl object-cover" style="position:absolute;height:100%;width:100%;left:0;top:0;right:0;bottom:0;color:transparent" sizes="(max-width: 768px) 100vw, 396px" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2F98d12fa7a1c626c805c2de91f5e19249b843b096-1080x1080.png&amp;w=640&amp;q=75 640w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2F98d12fa7a1c626c805c2de91f5e19249b843b096-1080x1080.png&amp;w=750&amp;q=75 750w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2F98d12fa7a1c626c805c2de91f5e19249b843b096-1080x1080.png&amp;w=828&amp;q=75 828w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2F98d12fa7a1c626c805c2de91f5e19249b843b096-1080x1080.png&amp;w=1080&amp;q=75 1080w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2F98d12fa7a1c626c805c2de91f5e19249b843b096-1080x1080.png&amp;w=1200&amp;q=75 1200w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2F98d12fa7a1c626c805c2de91f5e19249b843b096-1080x1080.png&amp;w=1920&amp;q=75 1920w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2F98d12fa7a1c626c805c2de91f5e19249b843b096-1080x1080.png&amp;w=2048&amp;q=75 2048w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2F98d12fa7a1c626c805c2de91f5e19249b843b096-1080x1080.png&amp;w=3840&amp;q=75 3840w" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2F98d12fa7a1c626c805c2de91f5e19249b843b096-1080x1080.png&amp;w=3840&amp;q=75"/></div><div class="flex flex-1 flex-col gap-4 max-lg:px-2.5 lg:pt-4"><div class="flex items-center gap-4"><div class="font-roboto focus:ring-ring inline-flex items-center rounded-lg font-medium transition-colors focus:ring-2 focus:ring-offset-2 focus:outline-none px-2 py-1 text-xs leading-normal bg-hc-sky-500 text-sky-50">Podcasts</div><span class="h-3.5 border-r border-r-black/30"></span><span class="font-medium font-roboto text-hc-slate/70 text-xs"><span>September 3, 2026</span></span></div><h3 class="font-roboto line-clamp-2 text-base font-medium">Ep. #93, Adaptive Sampling Strategies with Mike Goldsmith</h3><p class="font-roboto text-hc-slate/70 line-clamp-3 text-sm font-normal max-lg:hidden">On episode 93 of O11ycast, Mike Goldsmith talks to Martin Thwaites and Ken Rimple discuss Honeycomb&#x27;s contribution to the OpenTelemetry Collector, the Adaptive Tail Sampling Processor. This processor was created based on years of experience building Honeycomb&#x27;s custom, industry leading tail sampler, Refinery.
</p></div></div></a><a class="flex-1 lg:h-[245px]" href="/resources/leading-with-observability/leading-with-observability-scaling-fin-to-2x-engineering-productivity"><div class="bg-hc-gray-50 flex h-full flex-col gap-6 rounded-4xl p-2.5 lg:flex-row"><div class="relative h-[172px] w-auto md:h-[177px] lg:h-[225px] lg:w-[396px] xl:flex-1"><img alt="Title slide for the series &quot;Leading With Observability,&quot; Episode 1: &quot;Scaling Fin to 2x Engineering Productivity,&quot; featuring Darragh Curran (CTO, Fin) and Charity Majors (CTO, Honeycomb)." loading="lazy" decoding="async" data-nimg="fill" class="aspect-video rounded-4xl object-cover" style="position:absolute;height:100%;width:100%;left:0;top:0;right:0;bottom:0;color:transparent" sizes="(max-width: 768px) 100vw, 396px" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fca5665e8cba53cfd097fff94757ec1479ad2bf31-1920x1080.jpg&amp;w=640&amp;q=75 640w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fca5665e8cba53cfd097fff94757ec1479ad2bf31-1920x1080.jpg&amp;w=750&amp;q=75 750w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fca5665e8cba53cfd097fff94757ec1479ad2bf31-1920x1080.jpg&amp;w=828&amp;q=75 828w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fca5665e8cba53cfd097fff94757ec1479ad2bf31-1920x1080.jpg&amp;w=1080&amp;q=75 1080w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fca5665e8cba53cfd097fff94757ec1479ad2bf31-1920x1080.jpg&amp;w=1200&amp;q=75 1200w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fca5665e8cba53cfd097fff94757ec1479ad2bf31-1920x1080.jpg&amp;w=1920&amp;q=75 1920w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fca5665e8cba53cfd097fff94757ec1479ad2bf31-1920x1080.jpg&amp;w=2048&amp;q=75 2048w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fca5665e8cba53cfd097fff94757ec1479ad2bf31-1920x1080.jpg&amp;w=3840&amp;q=75 3840w" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fca5665e8cba53cfd097fff94757ec1479ad2bf31-1920x1080.jpg&amp;w=3840&amp;q=75"/></div><div class="flex flex-1 flex-col gap-4 max-lg:px-2.5 lg:pt-4"><div class="flex items-center gap-4"><div class="font-roboto focus:ring-ring inline-flex items-center rounded-lg font-medium transition-colors focus:ring-2 focus:ring-offset-2 focus:outline-none px-2 py-1 text-xs leading-normal bg-hc-gray-200 text-hc-slate">Leading With Observability</div><span class="h-3.5 border-r border-r-black/30"></span><span class="font-medium font-roboto text-hc-slate/70 text-xs"><span>August 27, 2026</span></span></div><h3 class="font-roboto line-clamp-2 text-base font-medium">Leading With Observability: Scaling Fin to 2x Engineering Productivity</h3><p class="font-roboto text-hc-slate/70 line-clamp-3 text-sm font-normal max-lg:hidden">Darragh Curran, CTO at Fin (formerly Intercom), set out to double engineering productivity—and nearly tripled it instead, using AI-driven code, an AI-powered PR review system, observability as a trust mechanism, and much more hands-on leadership. In the first episode of Leading With Observability, Charity gets into the messy bits behind that transformation, not just the highlight reel.</p></div></div></a></div><div class="full flex flex-col gap-4 max-xl:mt-4 md:flex-col lg:flex-row xl:flex-1 xl:flex-col xl:gap-6"><a class="flex-1 lg:h-[130px]" href="/resources/webinars/aws-honeycomb-workshop-ai-investigators"><div class="bg-hc-gray-50 flex h-full gap-4 rounded-3xl p-4 xl:gap-6 xl:rounded-4xl xl:p-2.5"><div class="relative h-[84px] w-[84px] self-start xl:h-[135px] xl:w-[135px]"><div class="aspect-square h-full w-full overflow-hidden rounded-2xl bg-white xl:rounded-3xl"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 542 542" fill="none" class="h-full w-full"><path fill="#FBEDF0" d="M0 0h542v542H0z"></path><path fill="#F7D2D7" d="M460.213 394.786c38.019-55.443 16.608-179.375-78.922-214.08-56.823-20.676-114.265-101.902-218.578-66.827a195.6 195.6 0 0 0-8.51 3.138 160.962 160.962 0 0 0-8.235 3.692C38.36 174.244 36.576 301.622 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At the time, that demo took eight tool calls and about $0.60 of inference. A year later, we process about 2 million agent-initiated query runs through Honeycomb every month!\"]}],[\"$\",\"p\",\"e019a74f6b2e\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"What’s really interesting, though, is that we’ve seen agents start to accomplish significantly longer-horizon tasks. Frontier models—like Sonnet or Fable 5—are increasingly agentic and capable. In the same agent session, we’ve seen tool calls double on average between February and June of this year. 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I don’t think that’s true. The rise of the cloud didn’t destroy the idea of IT. The existence of Rails doesn’t mean we don’t need server programmers. Productivity increases expand the map. There’ll be more software, of all shapes and sizes. We’re going to need more of everything.\"]}]]}]\n47:[\"$\",\"p\",\"4e166c189cae\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"What’s really interesting is that \",[\"$\",\"em\",\"2be8c2cd67ed\",{\"children\":[\"hum"])</script><script>self.__next_f.push([1,"an-initiated queries haven’t shrunk\"]}],\". Agents aren’t taking investigations away from humans, they’re \",[\"$\",\"em\",\"df700adcbd82\",{\"children\":[\"accentuating\"]}],\" them. People still use the UI, people still check their boards, but they’re now able to do more than they could before by leveraging AI. We see this a lot in terms of edits—while people \",[\"$\",\"em\",\"2a8f3b7aa2e7\",{\"children\":[\"are\"]}],\" using agents to build boards and update SLOs, the overwhelming majority of tool calls are to just run queries.\"]}]\n48:[\"$\",\"p\",\"e50491bc86f1\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"In my blog, I claimed that productivity increases weren’t going to diminish the impact or effect of people, and that’s what the data shows. The map is not the territory, but the map has grown significantly thanks to agents.\"]}]\n49:[\"$\",\"p\",\"259099855539\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"This doesn’t mean that all of these agents are being driven by a human, though. We’re seeing an increasing amount of headless agents using our MCP; it’s actually the fastest-growing segment.\"]}]\n4a:[\"$\",\"h2\",\"85a4b15a886c\",{\"id\":\"claim:-it’s-only-getting-cheaper-to-do-this\",\"className\":\"text-h3 text-hc-slate not-prose mt-6 xl:scroll-mt-24\",\"children\":[\"Claim: It’s only getting cheaper to do this\"]}]\n4b:[\"$\",\"blockquote\",\"306b4db4bb33\",{\"className\":\"not-prose font-roboto border-hc-gray-200 text-hc-slate mx-2 mt-10 mb-10 border-l-2 pl-4 text-base leading-normal font-normal tracking-normal lg:mt-16 lg:mr-11 lg:mb-16 lg:ml-11 lg:pl-[31px]\",\"children\":[[\"$\",\"em\",\"24f47a857389\",{\"children\":[\"Inference costs are only going down…If your product’s value proposition is nice graphs and easy instrumentation, you are cooked. An LLM commoditizes the analysis piece, OpenTelemetry commoditizes the instrumentation piece.\"]}]]}]\n4c:[\"$\",\"p\",\"1e0b3e9a2c42\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"In my original post, I focused on the idea of inference costs going down. I think that’s, broadly, still true (even if a lot of people are getting sticker shock as they transition from all-you-can-eat to API pricing for development workloads). That said, I’m writing this post on a laptop that effortlessly runs Google’s Gemma4 model, and it’s perfectly capable of using our MCP. I think, long-run, the “cost of AI” is going to keep going down at a fixed level of capability.\"]}]\n4d:[\"$\",\"p\",\"8b03b66c1523\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"What I didn’t call, though, is that the robots are actually surprisingly efficient when it comes to \",[\"$\",\"em\",\"ddcf71902d4b\",{\"children\":[\"using\"]}],\" Honeycomb! On average, agent queries cost about half as much to serve as human ones. There’s a pretty easy explanation for this: the agents are able to much more accurately figure out what to search for, especially if they’re running with access to your code. They know exactly what to look at, and they don’t need to spend as much time searching across an entire environment to orient themselves.\"]}]\n4e:[\"$\",\"p\",\"002702986528\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"Interestingly enough, we also notice that agents—on average—tend to run queries over a 50% shorter time range than humans. Where this gets really interesting is how \",[\"$\",\"em\",\"0cbd653acccd\",{\"children\":[\"different\"]}],\" the agent runs tend to be. Custom agents tend to look at smaller time windows, while human-driven ones tend to look across longer periods of time. My hypothesis is that those custom agents are probably more task-oriented (“Here’s an "])</script><script>self.__next_f.push([1,"error, look around this time for traces”) vs. more exploratory work being done in the development loop.\"]}]\n4f:[\"$\",\"p\",\"d1ecac6616bd\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"The headline number, though? On average, an agent-initiated query fans out to 3.3x fewer lambdas, scans 1.8x fewer bytes, and burns 2.2x less compute. What does this mean for us? Well, from March to June we’ve grown 4x in query volume while only increasing cost by 23%, leading to a 72% reduction in unit costs for agent-initiated queries. I’ll take that!\"]}]\n50:[\"$\",\"$L2c\",\"adfd99143919\",{\"priority\":\"$undefined\",\"fetchPriority\":\"low\",\"loading\":\"lazy\",\"sizes\":\"100vw\",\"data-testid\":\"article-image\",\"className\":\"not-prose mt-6 max-h-[451px] rounded-[22px] block h-auto w-full\",\"src\":\"https://cdn.sanity.io/images/927dxq0h/production/f4a4555365d1b22f17ee5bf3c66bc36905701798-3840x2160.png\",\"alt\":\"Average per-query run of human-initiated queries compared to agent-initiated queries.\",\"width\":3840,\"height\":2160}]\n51:[\"$\",\"p\",\"3df4cefd6656\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"\"]}]\n52:[\"$\",\"p\",\"faf96082aee6\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"An interesting coda to this, though, is that agents are \",[\"$\",\"em\",\"99136bff3c54\",{\"children\":[\"significantly worse\"]}],\" for caching. While we don’t see significant amounts of caching for interactive queries in general, we see almost \",[\"$\",\"em\",\"cdcce552089d\",{\"children\":[\"none\"]}],\" for agent-driven queries. This is mostly because agents don’t need to ask the same question twice. I’ve also personally observed that agents prefer to re-run queries rather than reuse existing ones, which is interesting and probably deserves more attention.\"]}]\n53:[\"$\",\"p\",\"88e212570f74\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"That said, we’ve seen pretty \",[\"$\",\"$L25\",\"7b932d71542d\",{\"href\":\"/blog/30-70-prs-day-how-we-managed-not-wreck-systems\",\"className\":\"not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline\",\"children\":[\"staggering levels of adoption\"]}],\", especially in the enterprise. One of our larger customers read 62 PiB of data in one month's time \",[\"$\",\"em\",\"10b44001a2d1\",{\"children\":[\"exclusively\"]}],\" through agents, mostly through off-the-shelf tools like Claude Code.\"]}]\n54:[\"$\",\"p\",\"e327ea353e48\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"There’s a downside to this as well, though: a lot of those queries wind up being kinda slow, because the agents aren’t chewing through nicely structured data, they’re grepping across unstructured body fields. Structured, high-cardinality, high-dimensionality data is no longer a nice-to-have for humans, \",[\"$\",\"strong\",\"76d48d5b803d\",{\"children\":[\"it is a requirement for making agent investigations affordable\"]}],\". All of that data that the agent has to chew through in order to find the needle in the haystack? That’s tokens coming out of your pocket. Do you want to pay Fable rates to look through gigabytes of crappy logs?\"]}]\n55:[\"$\",\"h2\",\"c4d3a63416fe\",{\"id\":\"claim:-fast-feedback-loops-are-all-that-matter\",\"className\":\"text-h3 text-hc-slate not-prose mt-6 xl:scroll-mt-24\",\"children\":[\"Claim: Fast feedback loops are all that matter\"]}]\n56:[\"$\",\"blockquote\",\"eeeda0d7ef8d\",{\"className\":\"not-prose font-roboto border-hc-gray-200 text-hc-slate mx-2 mt-10 mb-10 border-l-2 pl-4 text-base leading-normal font-normal tracking-normal lg:mt-16 lg:mr-11 lg:mb-16 lg:ml-11 lg:pl-[31px]\",\"children\":[[\"$\",\"em\",\"124d04828ec8\",{\"children\":[\"I’m gonna put a marker out there: the only thing that really matters is fast, tight feedba"])</script><script>self.__next_f.push([1,"ck loops at every stage of development and operations. AI thrives on speed—it’ll outrun you every time.\"]}]]}]\n57:[\"$\",\"p\",\"b7c4f79692b3\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"Just go ask Claude to summarize the current state of the “Loops” discourse and you’ll see that the rest of the AI thought leadership industry is talking about something you read here a year ago.\"]}]\n58:[\"$\",\"p\",\"e101b5a00a41\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"Anyway, there’s no prize for being first, so I instead want to share a story from one of our customers. Guy writes in to tell us how much he loves MCP and how he’s using it. He’s hooked up everything to agents that talk to Honeycomb. When a customer reports an issue or someone files a bug, the agent goes out to verify it using production telemetry. 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