In this installment of our Partner Spotlight series, we’re continuing to showcase the innovators driving app and agent development forward on the Microsoft Marketplace. Each feature highlights the distinct journeys of partners who are pioneering AI-powered solutions, building across the Microsoft ecosystem, and delivering transactable applications that are shaping the future of the marketplace. In this article, I sat down with Alois Reitbauer from Dynatrace to learn more about their story and partner journey.
About Alios: Alois Reitbauer is Chief Technology Strategist at Dynatrace where he is responsible for the Edge Data Platform, open source, and research in cloud-native technologies, security, and large-scale data management. He was a founding member and contributor to several standards in the W3C (World Wide Web Consortium) regarding web performance and distributed tracing and CNCF TAG App Delivery. Alois co-founded OpenFeature, one of the fastest growing projects in the cloud-native space. He curates the open-source canvas, a modelling and strategic planning tool for open source projects. Alois advises startups and scaleups on open source and product commercialization strategies and is an early-stage investor with a focus on developer experience tooling.
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[JR]: Tell us about your organization. What inspired the founding? What products/services do you offer?
[AR]: At Dynatrace, we believe every business is a digital business, made up of data, analytics, infrastructure, and ops. We aim to help businesses harness all that information in context. Dynatrace provides observability and security with AI and automation, focusing on auto-prevention, auto-remediation, and auto-optimization. Our unified observability and security platform can analyze all your data in context, leveraging AI-powered analytics for predictive and preventive operations.
Davis AI, which is at the heart of our platform, combines causal, generative, and predictive AI technologies. Another foundational component is Grail, our causal data lakehouse featuring massively parallel processing. Together, with our other offerings — including AutomationEngine, AppEngine, OneAgent, PurePath, and Smartscape, among others — we empower customers to analyze, automate, and innovate faster with full-stack, AI-driven observability.
Our platform enables customers to address their most pressing challenges right out of the box. They can also automate any process by leveraging AI, dynamic mapping, and Grail to contextualize all their data for instant analytics and automation.
[JR]: Can you tell us a bit about the application(s) you have available on the marketplace? How does it work?
[AR]: Users can deploy our Azure-native integration from the Azure Portal with one click. It behaves like a native Azure service, with seamless authentication, billing, and visibility.
Our AI-powered observability platform is embedded in Azure. We offer our SaaS-based service via the Microsoft Marketplace. Customers can leverage everything our platform offers to monitor, protect, and optimize their entire application stack.
[JR]: What Microsoft cloud products did you use in your app or agent development? What value is this enabling with your customers?
[AR]: We use many different Azure Blob Storage, Azure Kubernetes Service (AKS), Azure Cache for Redis®, and Azure database services, as well as underlaying services for networking and DNS running in our components on Azure. We integrated these native Azure services with the Dynatrace Grail data lakehouse, our application framework, and our predictive, causal, and generative AI.
We also embedded our AI-powered observability directly into the Azure ecosystem. This helps businesses gain a deeper understanding into their cloud environments, opening the door for huge growth potential and the ability to scale their business with intelligent, data-driven insights.
With Dynatrace observability on Azure, we help customers understand their data in context, find answers in real time, automate their processes, and accelerate AI adoption.
[JR]: What inspired your team to begin integrating AI into your app or agent development workflows?
[AR]: We started working on using AI over a decade ago to make people’s lives easier and let them focus on what really matters. Our first use case for AI was to automatically detect the root cause of an application issue and surface it to the user.
Now, our Davis AI engine is at the core of our platform. It features the unique combination of causal, predictive, and generative AI to help businesses identify and address issues before they affect users, allowing them to spend more time innovating and less time firefighting. Our platform also helps organizations monitor, optimize, and secure generative AI applications, LLMs, and agentic workflows, which boosts performance, explainability, and compliance.
[JR]: How do you envision AI transforming your product offerings over the next 12–18 months?
[AR]: AI is at the core of our platform. Our AI engine, Davis, continuously looks for issues and provides precise root cause. This helps customers identify and resolve issues potentially in minutes — before they affect users and become expensive.
In the future, we see ourselves moving from finding and identifying issues to semi-automatically resolving them with the help of services like the Azure SRE agent. We are experts at solving application-level problems with modern AI workloads. But, with the help of cloud service providers like Microsoft, we want to deeply integrate and leverage their expertise to take Dynatrace to the next level.
[JR]: Can you walk us through your journey from concept to deployment for an AI agent?
[AR]: Everything starts with the use case. When we think about the customer’s needs, we usually define the level of autonomy from assisting, to advising and eventually delegating the goal to an AI agent. So, everything starts there. Then, we analyze what a human will need to perform the task and which tools are required. We use this knowledge to help with fine tuning our specific data queries. Tools will become software services exposed via Microsoft Cloud Framework (MCF).
[JR]: How do you ensure your AI agents are compliant, secure, and scalable for enterprise customers?
[AR]: As we were developing our first AI agents, we built dedicated observability for it. This led to a dedicated product solution for AI observability, helping us understand and debug behavior, insights into cost, performance and quality as well as evaluating guardrails to understand where agents do not work properly.
[JR]: How are you leveraging the Microsoft Marketplace to distribute your AI-powered solutions?
[AR]: Our AI-powered observability platform is natively available in Microsoft Azure as an Azure Native Service. Customers gain full-stack visibility, automation, and root-cause detection across Azure-native and hybrid environments. This helps to drive faster innovation, better customer experiences, and more efficient use of Azure.
With our observability platform natively integrated into Azure, we help to drive modernization, FinOps, and DevOps acceleration. This helps our customers get contextual answers and automation from day one.
[JR]: What measurable outcomes have you seen from deploying AI agents or Copilot-enhanced solutions?
[AR]: We see the highest impact on analytics and research-intensive tasks. Work that would otherwise take hours or days can now be done in minutes or less. For example, one of our customers — the technology arm of one of Thailand’s leading commercial banks — used Dynatrace’s full-stack observability and Davis AI to get a single source of truth that helped them identify and resolve the root cause of problems in real time. This cut the time it takes its teams to solve problems by 10x.
[JR]: What are your top tips for designing effective AI agents?
[AR]: The best advice is to solve a real problem. Identify the problem and what makes it hard to handle today, and then work your way toward an ideal solution. Very often, the best way to identify the issue is if you think, “If I only had someone who would do xyz,” that provides a good hint of what you want an agent to do. I also recommend prototyping early on against production data.
[JR]: What common pitfalls should other partners avoid when building with AI?
[AR]: You should not throw AI on every problem you see. Often, there are simpler solutions with equal or even better results for certain issues. Focus on where AI can really generate value. Many people overestimate the value of their AI projects without having a clear strategy to measure its value.
People also underestimate the cost of their AI applications. AI applications are usually more costly than “regular” applications, Therefore, cost tracking is critical. Additionally, sometimes agents behave in an undesired way and require you to debug in real time and in production. That’s why AI observability is key.
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