Connecting agent performance to product outcomes. Observe what users ask your AI agent, evaluate where it fails and what those cost your business. Agent Analytics is now available to all customers. Aug 20, 2026 No one builds AI agents just for the sake of it. You're ultimately looking to drive up product metrics that matter such as conversion, retention and monetization. Several tools exist to test your agent performance offline and build an evaluation suite against a great initial ground truth dataset. Things get way more complicated when your agent shifts to online sessions and production scale. As you race to launch AI agents now or in 2027, there are 3 new types of challenges you're going to encounter. * AI Fails Silently: Two users can ask an agent the same question and get completely different outputs. AI can hallucinate, ignore instructions and confidently give incorrect answers. Building non-deterministic products in production provides for a new suite of issues. * Measurement Gap: Amplitude, Inc. used to have pages, buttons and clicks making it easy to track from an analytics standpoint. So those users who spent a lot of time firing events and demonstrated retention were those that traditionally were fans. Now, two users can have long conversations and look equally engaged in a classic analytics dashboard. Who actually found it useful? Who thought it sucked? * Expensive Mistakes: This is the most expensive part of the user journey and mistakes here have hard infrastructure costs. Mistakes here have hard infrastructure and opportunity costs. Amplitude, Inc. faced the same problems when Amplitude, Inc. launched its own specialized agents and that's why Amplitude, Inc. created Agent Analytics. Amplitude, Inc. has been using Agent Analytics for its open-weight models experimentation and achieved Anthropic Sonnet-like performance from Fireworks AI Kimi at 2/3rd the cost. Since inception, its design partners such as The Economist, Hybrd and Cashbook helped put Agent Analytics through rigorous performance tests by instrumenting their own agents. Agent Analytics is generally available today and includes a usage allotment for all Amplitude plans (including free plans) along with the below value-add capabilities. Measure agent quality without the drama. One of the biggest hurdles AI teams face today to get started is defining a whole host of criteria to create custom AI evaluations (evals) in order to score online production sessions. Your team needs to define custom evals, wire up an LLM-as-a-Judge with a frontier model, and pay the token bill to score every production conversation. You must repeat this for every single quality dimension you want to measure. In contrast, point Agent Analytics at your agent and every session gets scored on the criteria teams normally spend days getting started: Did the agent complete the task? Task success rate is slowly becoming a north star metric for anyone running online evaluations, and you get it out of the box, for all sessions, with a rationale attached to every verdict. Amplitude provides these AI evaluations in the form of Signals to help you get immediate value: task completion, response quality, user intent, session safety, user friction, negative feedback, and data quality. Amplitude, Inc. run all of these for you, included on every Amplitude plan, so any platform customer can see real task-completion and helpfulness trends the same afternoon they instrument. 9,240 sessions

Amplitude names Angela Ferrante as SVP marketing. Amplitude has appointed Angela Ferrante as Senior Vice President of Marketing, adding a new senior marketing leader as the company continues to navigate an evolving digital analytics and product intelligence landscape. Ferrante brings experience across marketing, technology, and customer-focused growth. In her new role, she will lead Amplitude's marketing organization and contribute to the company's broader marketing strategy. Her appointment comes as businesses increasingly rely on digital product data to understand customer behavior and improve digital experiences. As organizations collect more information across websites, applications, and other digital touchpoints, marketing teams are also taking a greater interest in how product and behavioral data can inform customer engagement. The role of marketing leadership in the technology sector has also expanded. Beyond brand and communications, marketing executives are increasingly involved in positioning products, understanding customer needs, supporting go-to-market strategies, and connecting data-driven insights with business objectives. Ferrante's move to Amplitude comes against this backdrop. Her responsibilities will include guiding marketing efforts while working within an industry where analytics, experimentation, artificial intelligence, and digital customer experiences are becoming increasingly interconnected. The appointment also reflects the continued movement of experienced marketing executives into leadership roles at technology companies. As competition grows across the software and digital experience markets, companies are placing greater emphasis on marketing leaders who can connect technology with practical business outcomes. For the broader MarTech industry, the appointment highlights the continued importance of marketing leadership as companies work to connect product data, customer behavior, and business strategy in an increasingly digital environment.

Building got faster. Knowing what worked didn't. Code Mode and Data Assistant Agent help teams reason through what happened, trust the data behind it, and decide what to do next. Aug 13, 2026 Every product runs the same loop: build it, ship it, measure what happened, then use what you learn to build the next thing. The first half of that loop has become increasingly automated. CI/CD ships code. Feature flags control rollouts. AI can write, test, and iterate alongside developers. But turning what happens next into the right next move still depends heavily on human judgment. That judgment is the next thing to automate. Knowing why a metric moved, whether it matters, and what to do next requires more than access to data. It requires two things: the ability to reason through the context, and confidence that the underlying data is trustworthy. As more decisions shift from humans to agents, both become increasingly important. An agent that reasons well from bad context (or bad data) can still confidently make the wrong call. In Anthropic's June 2026 Economic Index report, researchers asked Claude users what they believed AI would never be able to do. The most common answers pointed to the same thing: judgment, contextual awareness, and situational reasoning - knowing what a situation calls for. Amplitude, Inc. see that as the next problem to solve: helping teams make better decisions with better context and more trustworthy signals. Today, Amplitude, Inc. is introducing two AI capabilities built for that problem: Code Mode and Data Assistant Agent. Code Mode helps teams reason more deeply about what their data shows: what changed, why it matters, who's affected, and what to do next. Data Assistant Agent helps make the data behind that reasoning easier to trust by identifying and prioritizing data-quality issues. Answer the questions behind your metrics. Most product teams can see when a metric moves. The harder questions come next: Why did it move? Which users drove the change? What behaviors predict what happens next? Did the thing Amplitude, Inc. shipped actually make a difference? Answering those questions often means exporting data, writing custom SQL or Python, or waiting for help from a data scientist. Code Mode brings that work directly into Amplitude. It runs SQL and Python against the behavioral data already in Amplitude, letting teams investigate questions that go beyond a standard chart without leaving their workflow. Teams are already using Code Mode to uncover hidden segments and behavioral patterns, compare journeys across cohorts, build custom visualizations, and catch discrepancies between systems that neither flags on its own. That means teams can move beyond questions like "How many people used this feature?" and start asking: * Which behaviors predict long-term adoption? * Where do different user segments get stuck? * Did this launch actually improve retention? * Which users are most likely to benefit from an intervention? And because every query and validation step is logged along the way, the analysis can be inspected, challenged, and rerun. Teams don't have to take an AI-generated conclusion on faith. Available to all Free, Plus, and Growth customers, Code Mode brings deeper analysis into Amplitude so teams can move from signal to explanation to opportunity without exporting data or waiting on a separate analysis cycle. Build a trusted data foundation. Deeper reasoning only works if the data underneath it can be understood and trusted. If event names are unclear, definitions are missing, similar behaviors are tracked in multiple ways, or important context lives only in someone's head, humans and AI can interpret the same signals differently. As more analysis shifts to agents, those ambiguities become more consequential: AI can only reason from the context your data provides. And it isn't hypothetical. In Hex's 2026 State of Data Teams survey, data quality and lack of trust was the biggest barrier data leaders cited to scaling AI - named nearly twice as often as cost, skills, or tooling combined. Data Assistant Agent expands Data Assistant with new intelligence, a broader set of prioritized data-quality recommendations, and an interactive experience for investigating and acting on those recommendations in chat. It reviews taxonomy, metadata, usage patterns, and project context to identify unclear, duplicated, or undefined events and properties. Then it explains why those issues matter and recommends improvements that make Amplitude data more trustworthy for both people and agents. Teams can: * Run an AI-specific audit * Investigate taxonomy issues * Ask for prioritization * Ask for explanations * Draft metadata improvements Now available to all Plus, Growth, and Enterprise customers with AI features enabled, Data Assistant Agent ensures teams adopting AI-powered analytics have reliable data for both humans and agents to make the right decisions about your product. Close the loop from measurement to action. AI has accelerated how products get built and shipped. Now the rest of the product development loop needs to catch up. Code Mode and Data Assistant Agent tackle the two things AI needs to make better decisions from product data: the ability to reason deeply about what the signals mean, and a data foundation reliable enough to reason from. One helps uncover what happened, why it happened, and what to do next. The other helps identify and improve the data-quality issues that could undermine those conclusions. Together, they turn measurement from the end of the process into what it was always supposed to be: the beginning of what you build next. Carmen DeCouto Director of Product Marketing, Amplitude Carmen DeCouto is a Product Marketing leader at Amplitude. She's launched AI products, built growth programs across the full user lifecycle, and made a career out of translating the technical into something people actually care about. With nearly a decade in data and analytics, she's developed a sharp instinct for where the industry is heading and how to build for it.