--- title: 2025 Optimizely Web Experimentation release notes slug: web-experimentation/release-notes/2025-optimizely-web-experimentation-release-notes docTags: createdAt: 2026-08-05T20:57:20.623Z --- - Optimizely Web Experimentation Subscribe to updates on this page using the notification and RSS feed options. ## December - Added improvements to the [AI variation development agent](docId:0aAtuf82Px4hgmN8FL_p-) by letting you describe the change you want Mark to make inside the **Element Change** window, making it easier to access the agent and update your elements faster. ## October - Released the [AI variation development agent](docId:0aAtuf82Px4hgmN8FL_p-), which helps you modify and update existing website elements, create new ones, and generate and apply enhancement suggestions, while maintaining brand consistency by automatically retrieving page styles.  - Added the [Experiment Review Agent](https://app.archbee.com/docs/i5HmL1zgJNYU5N1vi93sg/KiJTfQQjUsHKpV4igZ7Ea#q21_t) for Web Experimentation, which lets you use Mark to review your experiment configuration and recommend changes to maximize your odds of reaching statistical significance. ## September - Released the [new version of the Visual Editor](docId\:t7EgfK4gtH2cbr94E1RUS) that uses an overlay instead of an iframe, letting you directly interact with the site itself and adjust the visitor experience for your experiment. - Added the new Optimizely Reporting [Metric Impact Report](docId\:fFvrkQM-xwbKT0iP0wlar) dashboard for Experimentation, which aggregates data on the impact of your metrics. - Added the [sample size calculator](docId\:crWOj7epOrXlTmPocc4WN) to the Web Experimentation platform. This lets you estimate test duration, providing a more comprehensive view of experiment planning and eliminating the need for manual calculations of visitors per variation. ## July - [Contextual bandits](docId\:HEAGVaimK_ctRVAIDugxA) are now generally available for personalization campaigns. Contextual bandits help you deliver the most personalized variation to each user by dynamically re-allocating traffic based on the primary metric and user attributes. - Moved the checkbox to [enable sending experiment data to Google Analytics 4 (GA4) through Google Tag Manager (GTM)](docId\:riUIo7SuA5g4XVNhY7y_5) from the experiment-level to the project-level. - Released [ratio metrics](docId\:njoNA36ZPt_NpzbeCVtYL) for A/B and multivariate tests, which let you select different events for the numerator and the denominator to reflect business-specific key performance indicators, such as revenue per add-to-cart click or feature use per account. See [Create a ratio metric in the metric builder](docId\:cphstZe3xKhsY7WsnnLZK) for instructions. ## June - Added the ability to instantly [deploy a winning variation](docId:09D9FnRH4rae4dt7wuSk1). ## May - Added the ability to [upload a screenshot for each variation](docId\:KiJTfQQjUsHKpV4igZ7Ea). - Removed the `/metrics` endpoints from the [Optimizely API](docId\:ZAZ5ZXCZZoX11jsGkDjEZ). Use the [Metrics Hub Service API's /metrics endpoints](https://docs.developers.optimizely.com/web-experimentation/reference/listmetrics) instead. - Updated the Experimentation [Usage & Billing dashboards](docId\:NR4At0IGrDJGq2AUK2j3Y) to include monthly active users (MAUs) by experiment and project. - Released [Mark chat](docId:6gjTnr7RZXwvAzqSCkkZe) for Experimentation. Mark automates tasks, surfaces insights, and guides decision-making.  - Released the [Optimizely Agent Platform results summary](docId\:kgXKG7SBqviw06Wjq5aEW), which automatically summarizes your A/B test results in plain language. - Added the ability to ideate with Mark to [get test ideas](docId\:KiJTfQQjUsHKpV4igZ7Ea). ### Usage and billing update Effective May 7, 2025, access to Optimizely Agent Platform features across Content Marketing Platform, Web Experimentation, Feature Experimentation, Personalization, Content Management System (SaaS), Collaboration, and Optimizely Data Platform will transition to a credit-based usage and billing model. For a full list of Optimizely Agent Platform features, see [Optimizely Agent Platform and AI features](docId:51wG_l34ATUNcLespgC-G). ## April - Added the [Summary page](docId\:KiJTfQQjUsHKpV4igZ7Ea) to experiments, which provides a high-level summary of the experiment settings that you can download and share with stakeholders for easier reporting on the experiments you are running. - Added the ability to [conclude and deploy the winning variation of an experiment](docId:09D9FnRH4rae4dt7wuSk1) (beta). This reduces the need for developer resources and lets you allocate traffic to a winning variation. This feature also lets you deploy any variation of a concluded experiment. - Added the ability to [upload images to the visual editor](docId:7tM_zXfrTc6bB19wLURDw) from the Optimizely Digital Asset Management (DAM) library. You must have an Optimizely product that has DAM to use it with Optimizely Experimentation. - Added the ability to link a Collaboration hypothesis from a new Web Experimentation A/B test. See [Link an experiment in Optimizely Web Experimentation to a hypothesis in Collaboration](docId\:iN5OAGPyb6fufERty75Xv). ### Usage and billing update Effective May 7, 2025, access to Optimizely Agent Platform features across Content Marketing Platform, Web Experimentation, Feature Experimentation, Personalization, Content Management System (SaaS), Collaboration, and Optimizely Data Platform will transition to a credit-based usage and billing model. For a full list of Optimizely Agent Platform features, see [Optimizely Agent Platform and AI features](docId:51wG_l34ATUNcLespgC-G). ## March **Experiment menu configuration** - Updated the experiment menu. This UX/UI enhancement streamlines the experiment configuration workflow by providing more in-app guidance. This was grouped and ordered to guide users in configuring a test step-by-step, improving the usability of a test’s configuration. **Warehouse-Native Experimentation Analytics** Warehouse-Native Experimentation Analytics is now generally available. The integration brings the elements of warehouse-native Optimizely Analytics to Feature Experimentation and Web Experimentation. Teams can analyze experiment performance, identify winning variations, and conduct deeper analyses on experiments that ensure data security and privacy and avoid data duplication or movement. - Enhance experimentation results by integrating Optimizely Experimentation data with additional insights from the data warehouse. See [scorecard](docId:1C7JEHttnh9k9BdpYz7ii) for more information. - Specify key user interactions to assess engagement and evaluate impact using [custom events](docId\:q4qVDU4KoruVJsDqhniIQ). - Create specific experiment-focused metrics (such as [conversion](docId\:J86da9mjVBG75Ftd6b4dJ), [numeric aggregation](docId:6N0SMW8_8BGpDI0RCWG7R), [ratio](docId\:vtFqPBsIlcQExIRI852HE), and more). - Segment users by common behaviors into [cohorts](docId\:qqERbaNW3LL9kPlSp27Lr) for precise analysis and targeted insights. - Create custom [metrics](docId:9zga6mORJKvRK6j3OqmXG) and [derived columns](docId:1wi6-v9k1O0weCWNUPCsS) to transform data to gain deeper insights. - Use the [Stats Engine](docId\:e7e-S6BollFwcP1RX9YS_) to ensure reliable results and advanced analysis capabilities. - Use [CUPED](docId\:XGaqMc2-oZfQBf5GHk2lS) to reduce the impact of random variation and surface insights quicker. - Switch effortlessly between configuring experiments and conducting deep experimentation analysis from both Feature Experimentation and Web Experimentation. - Filter results by user segments, analyze trends over time, and track variation performance through designated funnels via [Experimentation Analytics ](docId\:I3Euo_-9H2N5vzYVOgkxd)** > Explore**. - Uses the [Opti ID Admin Center](docId\:VBLJux5kON1IQDdOU04cF) for user management, giving you a single login point to switch among your Optimizely products. See the [Opti ID documentation](docId\:gfcl33i3WnqFAwFi9CT6y) to learn more about how to use it. - Updated the Analytics UI to match Optimizely styling. [Learn more about Warehouse-Native Experimentation Analytics.](docId\:V1bCdnHVWF0iA32vvysMA) ## February Released AI variation summary. This lets you use[ AI to generate descriptions for your variations](docId:3AW5eWJycdmd4MsXroEHk), summarizing what element or custom code changes were made and providing the variation's purpose. You can better understand how changes affect variation results to decide about your experiments and campaigns. ## January - Released [experience templates](docId\:QANZ3gFOty4EplLUP9jba) (formerly extensions) for general availability, which lets you streamline campaign and experiment creation and reduce code duplication. You can use the pre-built templates provided by Optimizely to quickly create a campaign or experiment with little to no development or build your own. See [Get started with experience templates](docId\:QANZ3gFOty4EplLUP9jba) for information. - Enhanced how Preview mode loads by only querying the relevant pages associated with the experiment. This has led to improvements such as decreasing the load time for some users from 30 seconds to 3 seconds.