{"id":647578,"date":"2026-11-06T12:04:42","date_gmt":"2026-11-06T12:04:42","guid":{"rendered":"https:\/\/userpilot.com\/blog\/?p=647578"},"modified":"2026-11-06T12:08:23","modified_gmt":"2026-11-06T12:08:23","slug":"userpilot-agent-analytics","status":"publish","type":"post","link":"https:\/\/userpilot.com\/blog\/userpilot-agent-analytics\/","title":{"rendered":"How Do You Know If Your AI Agent Is Working? Meet Userpilot Agent Analytics"},"content":{"rendered":"<p>Today, we&#8217;re introducing <a href=\"https:\/\/userpilot.com\/ai\/agent-analytics\/\" target=\"_blank\" rel=\"noopener\"><strong>Userpilot Agent Analytics<\/strong><\/a>, a new way for product teams to understand how people are using their AI agents and whether those interactions are delivering value.<\/p>\n<p>This is a challenge we&#8217;ve experienced firsthand with Lia, our own AI agent at Userpilot. When we launched Lia in beta, we already had the analytics in place to <a href=\"https:\/\/userpilot.com\/blog\/how-to-measure-product-adoption\/\" target=\"_blank\" rel=\"noopener\">measure adoption<\/a> and retention. We could see who was using her, how often, and for how long. But we soon realized that these numbers only told us part of the story.<\/p>\n<p>For example, if someone spent ten minutes chatting with Lia, was that because they were getting useful insights or because they were struggling to get the answer they needed? We couldn&#8217;t tell without understanding what was happening inside those conversations.<\/p>\n<p>That experience shaped how we built Agent Analytics. We wanted to give product teams visibility into what users are asking and how those experiences connect to the rest of their product. And we&#8217;re excited to finally make that possible within Userpilot.<\/p>\n<p><iframe loading=\"lazy\" title=\"Measure the Real Impact of Your AI Agent with Agent Analytics\" width=\"640\" height=\"360\" src=\"https:\/\/www.youtube.com\/embed\/Eprvu4UJ74Y?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/p>\n<h2>Why we needed a different way to measure AI agents<\/h2>\n<p>Now zoom out from our experience to what we heard from other teams.<\/p>\n<p>Most teams we spoke with fell into one of two camps. They either weren\u2019t measuring their agent systematically, or they were relying on basic signals such as conversation volume, thumbs up\/down, and customer feedback. That matches what Lisa, our UX researcher, described from the product research behind Agent Analytics.<\/p>\n<blockquote>\n<div>\n<p style=\"margin: 0 0 20px 0;\">Engineering teams already have excellent tools for traces, latency, tokens, and cost. But those tools answer questions about how the system runs.<\/p>\n<p style=\"margin: 0 0 20px 0;\">As product teams, our questions are different.<\/p>\n<p style=\"margin: 0;\">What are people coming to the agent for? Do they return? Which requests cause problems? What happens after the conversation? Does using the agent change how someone uses the rest of the product?<\/p>\n<\/div>\n<\/blockquote>\n<p>So we built Agent Analytics to give agent conversations the same kind of product context we already expect from the rest of our <a href=\"https:\/\/userpilot.com\/blog\/product-analytics\/\" target=\"_blank\" rel=\"noopener\">product analytics<\/a>.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/11\/5341d037-a80e-40b2-8a22-2baf940fca2a.webp\" alt=\"Ai agent analytics userpilot\" \/><\/p>\n<h2>Learn about your agent with Userpilot Agent Analytics<\/h2>\n<p>When you open Agent Analytics, you&#8217;ll find an overview of who&#8217;s using your agent, how often they&#8217;re interacting with it, and whether they&#8217;re coming back.<\/p>\n<p>But you can also dig into what&#8217;s happening behind those numbers. For example, if a particular topic is receiving negative <a href=\"https:\/\/userpilot.com\/blog\/user-feedback\/\" target=\"_blank\" rel=\"noopener\">user feedback<\/a>, you can see how widespread the issue is, open the conversations involved, and investigate what went wrong.<\/p>\n<p>We wanted to make that kind of investigation straightforward, so you can move from spotting a pattern to understanding the individual experiences behind it.<\/p>\n<p>That involves answering questions such as:<\/p>\n<h3>Are people using it in the first place?<\/h3>\n<p>The first thing you&#8217;ll see in Agent Analytics is an overview of who&#8217;s using your agent and how often. You can track adoption across users and companies, conversation and prompt volume, and how many people return to use the agent again.<\/p>\n<p>You can also dig into the details. For example, which customers are your most active users? How long do their conversations typically last? And where in your product do people tend to open the agent?<\/p>\n<p>I personally find such capability particularly useful. If users frequently turn to your agent from the same page, it could be worth investigating what they&#8217;re struggling with there.<\/p>\n<p>We&#8217;ve also included a retention comparison so you can see whether people who use your agent are more likely to return to your product.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/11\/6c6022a5-78e6-4ddf-8734-0caa45834e65.webp\" alt=\"retention analysis for ai agents userpilot\" \/><\/p>\n<h3>What are users trying to do?<\/h3>\n<p>With Topics, we use AI to automatically group conversations based on what users are asking your agent to do.<\/p>\n<p>You can see which requests come up most often, how frequently people ask about them, and which topics receive positive or negative feedback.<\/p>\n<p>You can also create your own topics for things you specifically want to monitor. For example, we could track how often people ask Lia for help with adoption analysis and see whether those conversations are going well.<\/p>\n<p>For our product, marketing, and CS teams, this opens up another source of customer research. We can learn what customers need help with and spot recurring issues without having to read through every conversation or wait for someone to raise a support ticket.<\/p>\n<h3>Where is your agent letting users down?<\/h3>\n<p>We also wanted to make it easier to find conversations where the agent wasn&#8217;t doing what it was supposed to.<\/p>\n<p>Agent Analytics automatically detects common failure signals, including failed actions, fallback responses, contradictory answers, repeated responses, and handoffs to human support.<\/p>\n<p>You can see how frequently each failure occurs, which users are affected, and open the conversations to find exactly where things went wrong.<\/p>\n<p>For example, if someone asks your agent to export a report but the action fails, you can see that failure flagged and review the relevant exchange.<\/p>\n<p>And if there&#8217;s a particular behavior you&#8217;re concerned about, you can create a custom failure signal to monitor it across future conversations.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/11\/8e0c2676-5ed6-4b32-a80d-e2c2843019e3.png\" alt=\"Custom failure signal for tracking ai agents\" \/><\/p>\n<h3>And did the user care about the failure?<\/h3>\n<p>One decision we made when building Agent Analytics was to track frustration separately from agent failures.<\/p>\n<p>So we look at how people react during conversations. If someone keeps rephrasing the same question, rejects an answer, or starts using all caps or excessive punctuation, we flag those as frustration signals.<\/p>\n<p>You can then look at failure and frustration signals side by side to see which problems are affecting the customer experience.<\/p>\n<h2>Act on your agent insights without leaving Userpilot<\/h2>\n<p>What I also like about having Agent Analytics in Userpilot is that we can do something with what we&#8217;ve learned rather than leaving those insights sitting in a report.<\/p>\n<p>Say we notice that several customers are getting frustrated when asking Lia for help with a particular feature. Our product team can investigate what&#8217;s causing the issue, while our CS team can reach out to the affected customers before they even submit a support ticket.<\/p>\n<p>And we can take that a step further. Because conversation topics, failure signals, and frustration signals are available in Userpilot&#8217;s segmentation tools, we can create a segment of those users and target them with a relevant in-app message, banner, or survey.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/userpilot.com\/blog\/wp-content\/uploads\/2026\/11\/b00fc9c7-7cf2-4239-96c8-dc341b15cffc.png\" \/><\/p>\n<p>This is also where our <a href=\"https:\/\/userpilot.com\/blog\/userpilot-ai-agent-lia\/\" target=\"_blank\" rel=\"noopener\">AI agent Lia<\/a> can help us move things along. Instead of building everything manually, I can ask her to analyze the affected segment, recommend what to do next, and even create an in-app flow or survey for those users. I can then review what she&#8217;s built before publishing it.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/11\/ec38e27d-19dc-4adc-8407-7c35d71d6b93.png\" \/><\/p>\n<p>The same approach works for opportunities, too. If we notice a growing number of questions about a feature, we might decide to improve its onboarding or make the relevant documentation easier to find.<\/p>\n<p>That way, what we&#8217;re learning from our agents can directly inform how we improve the product and support our customers.<\/p>\n<h2>Come try this with us!<\/h2>\n<p>We&#8217;ve already learned a lot from using Agent Analytics to understand how people interact with Lia, and I&#8217;m excited to see what other teams discover about their own agents.<\/p>\n<p>If you&#8217;re already using Userpilot on a Growth or Enterprise plan, you can try Agent Analytics with 5,000 free AI session analyses. You&#8217;ll just need to connect your agent through our SDK to get started. You can also choose what conversation data to capture and mask sensitive information based on your team&#8217;s needs.<\/p>\n<p>And if you&#8217;re new to Userpilot, <a href=\"https:\/\/userpilot.com\/demo\/\" target=\"_blank\" rel=\"noopener\">book a demo<\/a> to see how Agent Analytics works alongside the rest of our product analytics and in-app engagement tools.<\/p>\n<p>We&#8217;d love to hear what you&#8217;re building and what you&#8217;re learning along the way.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Today, we&#8217;re introducing Userpilot Agent Analytics, a new way for product teams to understand how people are using their AI agents and whether those interactions are delivering value. This is a challenge we&#8217;ve experienced firsthand with Lia, our own AI agent at Userpilot. When we launched Lia in beta, we already had the analytics in [&hellip;]<\/p>\n","protected":false},"author":70,"featured_media":647579,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[7604],"tags":[],"class_list":["post-647578","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-userpilot-insider"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.2 (Yoast SEO v27.2) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>How Do You Know If Your AI Agent Is Working? Meet Userpilot Agent Analytics<\/title>\n<meta name=\"description\" content=\"Is your AI agent helping users? Discover how Userpilot Agent Analytics connects conversations with the rest of your product data.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/userpilot.com\/blog\/userpilot-agent-analytics\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How Do You Know If Your AI Agent Is Working? Meet Userpilot Agent Analytics\" \/>\n<meta property=\"og:description\" content=\"Is your AI agent helping users? 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