Datadog is expanding digital experience monitoring across production environments, rolling out autonomous synthetic test generation alongside unified performance telemetry to diagnose broken user conversion funnels.
The two additions to the vendor’s Digital Experience Monitoring (DEM) suite – named ‘Journey Monitoring’ and ‘Bits Testing’ – are entering technical preview for enterprise customers running the company’s real user monitoring (RUM), synthetic checks, and product analytics modules.
Both of the new tools target recurring operational disconnects between marketing teams watching conversion drops and site reliability engineers triaging infrastructure faults.
Yanbing Li, Chief Product Officer at Datadog, said: “We don’t think of AI as a feature you add to a product. We think of it as what should be running underneath everything—infrastructure, security, and now the user journey.
“Journey Monitoring and Bits Testing apply that idea end-to-end to find the journeys that matter and use AI to keep testing them as the product changes.”
Correlating telemetry across user journey topologies
Engineering teams frequently trace page faults while e-commerce operators monitor gross conversion metrics. Journey Monitoring merges those operational tracks into a shared console. The system plots real user activity alongside technical service level objectives (SLOs), lining up journey starts, completion durations, and drop-off percentages against RUM availability and synthetic uptime.
When transactions fail, teams track the blast radius across dependent workflows. The platform maps upstream and downstream navigation paths, illustrating how an authentication failure cascades into checkout and account management screens. Colour-coded badges highlight breached SLO thresholds on the experience map.
Engineers can drill down from high-level funnels into specific transaction variants. When an availability SLO breaches on a purchase form, operators pivot into Session Replay or the RUM Sessions Explorer to inspect the client-side error without abandoning the journey record. The platform also evaluates production traffic patterns continuously to identify emerging user paths, allowing teams to register new transactional flows as managed assets before customers register support complaints.
Goal-based autonomous validation for dynamic software
Scripted synthetic tests fail when developers modify front-end layouts or introduce non-deterministic elements. Bits Testing counters that brittleness through autonomous discovery runs.
Quality engineers provide a target web address or supply natural-language instructions, prompting an agent to traverse the application, map viable transactional routes, and produce runnable test collections covering browser interactions, network calls, and APIs.
The system pairs traditional deterministic verification with goal-based synthetic runs. Deterministic routines validate strict sequences such as payment processing endpoints, authentication contracts, and fixed checkout logic.
Goal-based routines store natural-language objectives rather than static click sequences. During an active run, the testing agent determines viable pathways dynamically to reach directives such as completing round-trip flight reservations or retrieving answers from conversational support assistants. If an interface alteration moves a submission button or a recommendation engine serves variable components, the agent adapts its route to verify the end outcome.
“Datadog provides visibility and operational control as AI creates complexity,” explains Li. “We do so from two directions: agentic capabilities like Bits Testing that continuously test journeys, and those like Journey Monitoring that autonomously discover and observe the most critical customer paths.”
Both tools are available in preview to existing Datadog customers.
Datadog is a key sponsor of this year’s AI & Big Data Expo Europe in Amsterdam.
See also: SmartBear embeds BearQ testing agent in Atlassian Jira
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