Dynatrace has signed a definitive agreement to acquire Arize, a leader in AI observability, in a cash and stock transaction valued at $915 million. The deal will enable customers to evaluate, operate and improve AI applications from development through production at scale. Dynatrace will pay approximately $815 million in cash plus replacement equity awards for Arize employees. The company plans to fund the acquisition through cash on hand or its existing credit facility. The transaction is expected to close later this quarter or early in Dynatrace's third quarter, subject to regulatory approvals. Arize's two founders, Jason Lopatecki and Aparna Dhinakaran, will join Dynatrace at closing, with Lopatecki continuing to lead the Arize team. Dynatrace expects the acquisition to be approximately 200 basis points accretive to ARR growth and 175 basis points dilutive to non-GAAP operating margin for fiscal 2027.

Dynatrace acquires Arize, accelerates AI observability efforts. Published August 13, 2026 Dynatrace acquired Arize, which specializes in AI observability and the AI development lifecycle, in a deal worth $915 million. The acquisition is just the latest data point indicating Dynatrace has momentum. Arize has a strong open source community and works across all major AI frameworks. Dynatrace, a leading observability platform, said Arize will give it the ability to continuously improve AI applications across the lifecycle and connect them to infrastructure health and business processes. Rick McConnell, CEO of Dynatrace, said Arize "advances our AI observability leadership, accelerates our roadmap, enhances our long-term growth profile, expands our reach with the developer community." The deal is expected to close in the third quarter. Arize's two founders, Jason Lopatecki and Aparna Dhinakaran, will join Dynatrace and report to McConnell, who noted that Arize brings an AI-native team to Dynatrace. Dynatrace recently acquired Bindplane, an open telemetry data collection player, and DevCycle, which supports the open standard for feature flags. Dynatrace's strategy is to focus on open standards and interoperability. Constellation Research analyst Mike Ni said: "Dynatrace buying Arize signals that observability vendors are angling to become a key control plane for autonomous operations. The value shifts from monitoring whether systems work to understanding whether AI decisions were right, what they changed, and whether the business improved. Like other observability vendors, Dynatrace understands that whoever owns that decision trace owns the learning loop, and increasingly, the trust layer required to scale enterprise automation." Expanding footprint. Speaking at an investor conference, Dynatrace's McConnell said observability is going into a new era and evolving quickly. McConnell's take is that observability is a category that only becomes more important amid AI applications. Enterprise software is being sorted into AI winners and losers and it's clear "AI workloads require more observability not less." "What is evolving was a market that has existed for a couple of decades in observability oriented around what I would think of as business resilience. You need to make sure that your software is always running, that it's always optimized, that it's effectively delivering against its expected requirements. In an AI observability world, it's evolving to answer a couple of incremental questions. If the first question around business resilience is, is it running or is it working, then in an AI observability land, is it right or is it accurate is the information that an AI workload is going to an LLM to extract actually, the right information that would be given to an end user of that particular customer. And so AI observability is extending the requirements of observability overall." Dynatrace's heritage in application performance management was built on tracking traces and that foundation can now extend into AI observability. Arize accelerates the move into AI workloads and production code and the optimizations that follow. "You start with end-to-end observability, that's the foundation. You bring together all of these multi-vendor solutions into a common and integrated platform. That enables you to have the insights and answers you need to action those through a series of agents. And those agents can then ultimately lead to what we think of as autonomous operations," said McConnell. Consolidating platforms. The fiscal first quarter earnings indicate that Dynatrace is starting to consolidate wallet share. McConnell said he has talked to one customer who had 16 observability tools and inflated log tracking costs. McConnell added that Dynatrace is often in brownfield implementations where its platform consolidates legacy tools. A few use cases for Dynatrace: * A customer used Dynatrace as an operational system of record while building a custom CRM application through AI-assisted development to generate 7 figures of savings. * A digital insurance provider used Dynatrace AI observability to reduce onboarding time and identify an outdated model that was eating up the token budget. * Another customer used Dynatrace to validate model consumption, control cost and maintain data lineage from prompt to response. Dynatrace's platform features Grail and Smartscape, which provides a context layer and unified understanding of system relationships and behavior. Dynatrace Intelligence turns that understanding into action and combines deterministic processes and agentic AI. The company reported fiscal first quarter earnings of $36.65 billion, or 12 cents a share, on revenue of $555 million, up 16% from a year ago. Non-GAAP earnings were 48 cents a share. Total annual recurring revenue at the end of the first quarter was $2.14 billion, up 17%. new logo ARR was up 41%. Dynatrace projected second quarter revenue of $565 million to $570 million, up 14% to 15%, with non-GAAP earnings of 48 cents a share to 49 cents a share. Fiscal 2027 revenue will be between $2.3 billion to $2.32 billion with non-GAAP earnings of $1.97 a share to $1.99 a share. "We are seeing AI contribute in three ways. Increasing consumption across our platform, creating demand for new AI observability capabilities and directly monetizing agent usage," said McConnell, speaking on Dynatrace's earnings call. "The observability market has entered a new era. Software that once took months to build now ships in days. AI agents are taking autonomous action across infrastructure and enterprise customers are now deploying AI rapidly, not because every risk has been resolved, but because standing still means falling behind." Editor in Chief of Constellation Insights Constellation Research Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context... Results. 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From Signal to PR: What if your agents got better every time they failed? Published july 29, 2026. Co-Authored by Chris Cooning, Head of Product Marketing & Sally-Ann DeLucia, Head of Product & Jason Lopatecki, Co-founder and CEO & Aparna Dhinakaran, Co-founder & Chief Product Officer. What if your agents got better every time they failed? It's 2 AM, and the pager goes off. User frustration rates are climbing, so someone opens a laptop, and the clock that matters (time to fix) has barely started. The eventual patch may be two lines, but the expensive part comes first: finding the right traces, reconstructing the failure, forming a theory, and locating the responsible code. Arize is launching Signal, a managed agent built into Arize AX that takes on that investigative work continuously. Signal reviews production traces, identifies recurring failure patterns, and groups them into ranked issues. Each issue includes supporting evidence, a root-cause analysis, and a proposed fix. Signal doesn't stop at surfacing issues. With a repository connected, a managed agent can carry the investigation into the codebase, propose a fix, and open a pull request, turning production telemetry into a reviewable change. This launch points toward a more ambitious future. Arize believe agents will not only run in production, but also help improve the systems they run in. In fact, Signal is part of the agent improvement loop in Arize AX where production behavior becomes evidence, evidence becomes an investigation, and the investigation becomes a reviewable change. The engineer still makes the call; they just begin with a diagnosis instead of a blank query box. Tl;dr. * Signal finds the issue. It continuously reviews production traces, groups related failures, and surfaces the evidence, likely cause, and proposed fix. * Managed Agents help close the loop. With repository access, they can inspect the relevant code, propose a patch, and open a pull request. * Agent Studio makes the loop configurable. Teams can run guided or custom investigations once, on a schedule, or in response to an operational trigger. * Humans remain in control. The agent investigates and proposes; the engineer reviews and ships. Signal ships on Arize AX Free and Pro today. Full managed agents, including Agent Studio, presets, and repository access, are available to Enterprise customers in beta. Observability has a new reader. For decades, production telemetry had one real consumer: a human. Applications emitted logs, metrics, and traces, and dashboards organized them. Alerts woke someone up, and an engineer then translated that telemetry into an explanation. Coding agents accelerated the final step. Once a developer understood the problem, an agent could write the patch for them. But the human still had to consume the telemetry, isolate the failure, and turn the investigation into a prompt. Signal moves the agent upstream. This is the next step toward self-improving software: the telemetry you capture stops being something only a human reads and becomes an input an agent can immediately act on. From traces to action. * Evidence comes from traces and evaluations. Traces capture what the agent did: model calls, retrievals, tool executions, inputs, outputs, timing, and errors. Evals help distinguish a technically successful run from a good result. * Context connects runtime behavior to its cause. That can include logs, application spans, metadata, and, when connected, the repository where the change needs to land. * A trigger determines when the investigation runs. Signal continuously sweeps new production traces, while broader managed-agent workflows can run on a schedule or in response to an operational event. Together, those pieces create an improvement loop: * Investigate * Propose * Review * Ship * Observe again This makes observability, especially tracing, more important. Traces serve as the source of truth and the backbone of the feedback loop. The higher the quality of the traces, the better the agent can investigate failures and identify the right code to change. Paired with strong evaluations that distinguish error modes from silent quality issues, they help the agent turn "this behavior is broken" into "this is the code that should change." Start with Signal, extend with Managed Agents. Signal comes preconfigured for continuous production reliability. Turn it on for a tracing project, and it begins reviewing new runs, tracking issues it has already seen, and surfacing emerging failure patterns. For teams that want to go beyond that workflow, Managed Agents and Agent Studio in Arize AX include templates for investigating failing traces, triaging monitor alerts, analyzing costs, running recurring health checks, inspecting repositories, and proposing pull requests. Teams can also start with a blank agent and build around virtually any engineering workflow they need. Signal is not the endpoint, though. It is an early piece of a broader shift toward self-improving agent systems: software that can observe its own behavior, identify where it is failing, and propose the next improvement for a human to approve. The future is not an agent rewriting itself in production. It is a controlled loop in which every trace can become evidence, every failure can become an investigation, and every investigation can become a reviewable change. Signal is available in Arize AX today. Turn it on against the traces and evaluations you already capture.