Imagine commissioning le Carré and Fleming to build your perfect undercover agent: quietly embedded in the system you’re watching. You hand in your mission brief, which includes the target, objective, and behaviors to track. Your agent observes without drawing attention, reporting insights back to you. On cue, the information flow you’ve carefully orchestrated turns signals into actionable intelligence that helps pre-empt risk.

Dynatrace doesn’t write spy fiction. However, even better, Dynatrace now lets you write your own smart agentic workflows that deliver intelligent reports and react to changes in your environment based on your objectives.

Adding generative AI to your workflow

Using the power of gen AI, Davis CoPilot® transforms your workflows into agentic instructions. Davis CoPilot lets you explore data using conversational language, translating complex data and queries into summaries, and provides intelligent recommendations across Dynatrace.

Integrated into Dynatrace Workflows, Davis CoPilot is your toolkit for building smart automations, bringing the power of generative AI into your mission-critical workflows.

By embedding Davis CoPilot in your workflows, you can associate any automation with any number of conversational automations. Your workflows can even perform actions autonomously when combined with precise Davis® AI forecasting, for instance, scaling resources based on forecast demands.

In real time, these workflows monitor live data, summarize critical issues, and identify remediation paths or emerging threats. When scheduled, these smart workflows help you outsource routine tasks, such as alerting stakeholders of costly queries.

Let’s look at some examples of how these smart workflows can help you in your daily work.

Build agentic workflows that respond to critical events

Proactive guiding through complex problem remediation

Let’s assume you want to build an automation that cuts through alert noise and analyzes a problem as it occurs, guiding you through the remediation. When a new problem is detected, Davis CoPilot extracts the problem details, summarizes the situation, and provides tailored remediation guidance. By embedding it into a smart workflow, you can select your preferred automation to automatically syndicate this information, populate a ticket in ServiceNow or Jira, or post it to a dedicated Slack channel.

See how you can set up a workflow automation that automatically sends summaries and remediation guidance when a new problem is detected.

Monitor emerging threats to help you orchestrate a response

Next, you can build an agentic workflow that helps you monitor emerging threats and assess their risk to your environment as vulnerabilities are detected in your tenant. In plain language, you instruct your agent to extract IOCs, query security events in your environment, and correlate them with observability data in your environment. Information provided by the external threat feed is automatically matched against the live context in your tenant. The agent has now collected all the necessary information and provides a reliable risk assessment, along with a plan to orchestrate a response, directly in your Slack channel, ensuring around-the-clock visibility and a rapid response.

Write the future: Build an agentic workflow that autonomously auto-scales your resources

Dynatrace helps you build agents that reason autonomously. The key is to deliver data as precise as Dynatrace forecast capabilities. In this example, we linked the power of Davis AI to forecast demand, with generative AI and GitHub automations. Davis AI predicts the number of resources the hyperscaler infrastructure will need based on forecasted demand. When Davis AI notices a scaling need, Davis CoPilot interprets the data and autonomously edits the manifest using the GitHub automation. Giving you one end-to-end workflow that automatically scales resources up or down based on forecasted needs. To see this in action, watch how this workflow autonomously edits a manifest based on Davis AI suggestions to auto-scale a Kubernetes cluster.

Schedule agentic workflows to optimize routine tasks

Do you feel like sleeping in a little later? Maybe stretching your lunch break a little longer? Running that extra hill without sacrificing your productivity? Scheduling Davis CoPilot into your smart workflow is a great way to automate recurring tasks and save time.

Build an automation that predicts resource consumption

A recurring challenge for SREs is analyzing the full environment to predict future bottlenecks or over-resourcing and continuously translating the data to update stakeholders. Even with great observability in place, you need to ensure that you interpret the data and make timely decisions to inform future provisioning.

By combining Davis AI forecasting automation with Davis CoPilot, you can build an agent that answers key questions, such as which workloads are most resource-intensive, which resources show the most variance, and which require frequent scaling. This automation is capable of highly reliable forecasts, even when data points are limited. The automation interprets the data and emails actionable recommendations directly to you and anyone else who needs to stay informed.

To see this in action, watch the section of this video that explores predicting resource consumption.

Smart workflows that optimize query costs

Scheduling tasks can even help you keep costs lean and efficient. For admins or budget owners, staying within financial limits while maintaining performance is a constant challenge. In this example, we built a smart workflow that identifies the top 20 most expensive queries from the last 24 hours. Davis CoPilot analyzes each query and sends optimization recommendations directly to the query authors via email.

To implement this yourself, tailored to the most expensive queries executed on your tenant, go to our documentation

Conclusion: Adapt your workflows to any stage of your automation journey

These are just a few examples; the applications for it are endless. We’ve designed this workflow action to cater to your organization’s automation appetite. You may want to transform how you keep business stakeholders informed about what’s happening in your environment, leveraging Dynatrace’s highly accurate insights, which are translated into plain language and actionable next steps.

Alternatively, you may be ready to transition towards autonomous operations, where automation not only supports but also acts in a controlled and reliable manner. Davis CoPilot embedded into your workflows opens the door to your agentic journey.

Start your agentic journey and join the Davis CoPilot for Workflows Preview

Davis CoPilot for Workflows is available as a Preview. Sign up now and see how generative intelligence embedded into your workflows transforms your automation. Today, it helps you react faster, optimize more effectively, and collaborate seamlessly. Tomorrow, it will go even further: anticipating needs, orchestrating actions, and enabling truly autonomous reasoning.

The post Write the future: Create your own agentic workflows appeared first on Dynatrace news.

Deploying and safeguarding software services has become increasingly complex despite numerous innovations, such as containers, Kubernetes, and platform engineering. Recent global IT outages, such as the CrowdStrike incident, remind us how dependent society is on software that works perfectly.

Organizations strive to strike a delicate balance between cost, time to market, and innovation. This challenge is more pressing than ever as businesses seek to stay competitive while ensuring their software remains robust and secure.

This necessitates a comprehensive platform that empowers enterprises to understand IT and software within the broader context of their business operations, giving them confidence that their software and IT infrastructure are reliable.

Scale with confidence: Leverage AI for instant insights and preventive operations

Using Dynatrace, Operations, SRE, and DevOps teams can scale efficiently while maintaining software quality and ensuring security and reliability. Its AI-driven exploratory analytics help organizations navigate modern software deployment complexities, quickly identify issues before they arise, shorten remediation journeys, and enable preventive operations.

We’ve added numerous enhancements to our platform, leveraging advanced AI and automation for smarter software observability.

In this blog post, we show you how to

• Get AI-driven insights directly on your operations dashboards
• Improve MTTR with AI-assisted problem analysis and logs and traces in context
• Leverage Gen AI through Davis CoPilot to get insights into root causes
• Automate remediation of AI-detected problems with simple workflows
• Adopt Preventive Operations with AI forecasting and automated action

Get AI-driven insights directly on your operations dashboards

A high-level, customizable view of your data is crucial in modern software operations. Dynatrace Dashboards, powered by Grail™ data lakehouse and Davis® AI, offer precisely that. They provide a comprehensive overview, seamlessly integrating health and problem-related information into a single view. You can chart your topology across data silos alongside all alerts, events, and problems using honeycomb tiles, which offer convenient drill-downs into the problem-debugging user flow.

Dynatrace ensures that context is seamlessly integrated into the platform, thus simplifying complexity for you as a user when analyzing issues and allowing you to focus on what truly matters. AI-driven analytics transform data analysis, making it faster and easier to uncover insights and act. This approach not only improves user experiences, it ensures that critical insights are accessible to both experts and novices. By simplifying remediation journeys and extending features to more user groups, Dynatrace enables results across all teams.

When a truly important issue stands out, the next step is refinement. With a few clicks, you can segment and filter your data to focus on specific applications, assignment groups, or regions. Directly mapping and surfacing ownership information within data segments accelerates incident assignment notifications and triggers automatic remediations.

If you see an issue or need to look closely at a specific application where an issue was identified, simply select the element to be seamlessly directed to the Problems app. There, you can dig deeper while continuing to focus on your selected segment. This tight integration, following a golden thread of insights, ensures that you’re more productive. To experience the possibilities of AI-empowered dashboards, try our example dashboard on the Dynatrace Playground.

Improve MTTR with AI-assisted problem analysis, logs, and traces in context

The Problems app delivers opinionated AI-assisted problem analysis optimized for Operations and Site Reliability Engineers (SREs) and developers. According to IDC, guiding users visually and automatically surfacing all critical details enables a 56% faster mean time to repair (MTTR) for critical incidents.

When a large-scale incident occurs, follow the red flag that Davis AI uses to identify the root cause, pinpoint all relevant details, and visually reproduce the details in charts, highlighting the affected deployment.

Besides identifying the root cause, Davis AI also automatically connects all relevant log lines. Logs are invaluable for identifying further insights and detecting fundamental flaws, such as process crashes or exceptions. With a single click in Problems, all incident logs are surfaced automatically. But we don’t stop there, Dynatrace also seamlessly integrates relevant trace data, offering full visibility into even complex, microservices-based architectures.

By providing these end-to-end insights, Dynatrace and Davis AI empower SREs, developers, and architects to quickly dive deep into an incident’s details, including all relevant logs and traces. Using this context, they can effectively focus on fixing and remediating code-level issues, significantly improving MTTR, and ensuring that critical incidents are resolved swiftly and efficiently.

Leverage GenAI via Davis CoPilot for insights into root causes

Dynatrace offers precision tools for domain experts to solve complex problems and dig deeper into their data. While product owners often focus on the intricate technical details of an incident, they often prefer a quick summary of what happened and what caused it. The soon-to-be-globally available Davis CoPilot™ bridges this gap by summarizing problems and their root causes and suggesting remediation steps based on these insights.

You’re not limited to one problem; Davis CoPilot can simultaneously analyze multiple problems, draw conclusions about their relationships, identify the common root cause, and propose corrective steps. Instead of relying on a team of experts and waiting hours for insights, Davis CoPilot helps you identify similarities and draw relevant conclusions independently and efficiently.

The use of generative AI adds significant value by augmenting Dynatrace-detected technical root causes with knowledge from the global tech community. Generative AI can access and synthesize vast amounts of information from various sources, providing a broader context and deeper insights. This ensures that your teams benefit from the latest advancements and solutions, enhancing their ability to resolve issues effectively and efficiently.

Automate remediation of AI-detected problems with simple workflows

To automatically remediate Davis AI-detected problems, Dynatrace leverages powerful Workflows. Dynatrace workflows can be triggered by any problem or alerting event, automating domain-specific tasks to take remedial actions.

For example, workflows can scale up capacity to adapt to demand or automatically restart a service in case of a crash. With a large catalog of available workflow actions, you can react efficiently to AI-detected problems, reducing mean time to repair (MTTR) by automatically remediating issues.

But you can do much more with it: The recently introduced Simple Workflows, which are included in your Dynatrace subscription with no extra cost, offer greater flexibility and power than standard notifications. You can use the same mechanisms and trigger types to notify your developer team via Slack, create a JIRA issue, or send a PagerDuty alert.

This ensures that your operations, SRE, and DevOps teams can focus on more strategic tasks while the system handles routine problem resolutions. Automation enhances operational efficiency and ensures that your systems remain robust and reliable, even in the face of unexpected issues.

Adopt Preventive Operations with AI forecasting and automated action

Going beyond reactive problem detection, analysis, and remediation, Dynatrace can also leverage predictive AI to anticipate and avoid critical situations before they occur. Using Davis AI forecast, you can easily predict future capacity demands. Combining this knowledge with workflows allows you to take proactive measures to ensure system stability and performance.

Let’s have a look at a concrete example:

It’s easy to predict key indicators of your application, such as order levels or service request counts. Once load and demand rise and Davis AI identifies a potential future issue in your infrastructure setup, Davis CoPilot can automatically generate an updated Kubernetes configuration script for you and automatically upscale the environment to meet future demand. This ensures that your system scales appropriately to handle the anticipated demand, preventing incidents before they occur and eliminating the need to generate a problem.

That’s what we call Preventive Operations. Instead of sending an alert and notifying people, Dynatrace simply fixes the issue. According to Gartner’s Analytics Maturity Model, using predictive AI can significantly reduce the likelihood of incidents by taking preemptive action and remediation.

Start using Davis AI to analyze your environments and predict and address potential issues in advance. This will empower your teams to avoid potential problems and ensure a smooth, uninterrupted user experience.

Tackle business challenges with confidence

Ensure your software runs securely and reliably with Dynatrace and Davis AI.

Dynatrace and Davis AI support you by running your software securely and reliably. This includes advanced root cause analysis, deep insights into detected issues, and corrective actions—whether manual or automatic—to prevent outages before they occur.

Get started

For more information, have a look at our documentation or explore the available resources on the Dynatrace Playground to experience some of these enhancements first-hand:

• Investigate our Problems dashboard or have a look at the Problems app on the Dynatrace Playground
• Experience the Davis AI forecasting capabilities embedded in Dashboards
• Watch a step-by-step video and learn how to proactively scale out your Kubernetes environment
• Dig deeper into our documentation for Davis CoPilot, Problems, or Simple Workflows

The post Powerful exploratory analytics for AI-driven insights appeared first on Dynatrace news.

We’re excited to announce that Davis CoPilot Chat is now available across the Dynatrace platform. Davis CoPilot™, launched in October 2024 to support Dynatrace users with access to their data, now extends across the platform, streamlining user onboarding and providing comprehensive support and contextual insights from various Dynatrace® Apps. With the new Davis CoPilot conversational interface, users can leverage natural language to quickly get answers to their questions, making it easier than ever for users to interact with Dynatrace.

Intuitive access to information boosts team productivity

We understand that taking advantage of the numerous features and functionalities offered by platforms like Dynatrace can be challenging. To help you navigate this and boost your efficiency, we’re excited to announce that Davis CoPilot Chat is now generally available (GA). This new feature provides information and guidance exactly when and where you need it, making your Dynatrace experience smoother and more efficient.

Davis CoPilot leverages the power of generative AI to answer your questions through a globally accessible chat interface. We’re proud to say that Davis CoPilot is multilingual: you can ask questions and get answers in many different languages, including French, Spanish, German, Portuguese, Chinese, Japanese, and, of course, English. Davis CoPilot provides immediate, accurate responses, eliminating the need for extensive searches and reducing dependency on support channels. This makes knowledge more readily available and boosts productivity and user experience for both new and experienced users.

Davis CoPilot Chat follows our recent announcement of the general availability of Quick Analysis in Notebooks and Dashboards, which makes data accessible to technical and non-technical users alike. This means you can interact with data stored in the Dynatrace Grail™ data lakehouse just by using natural language.

Simplify onboarding and quickly find what you’re looking for with Davis CoPilot

You can start using the Davis CoPilot conversational interface immediately. Simply enable Davis CoPilot and assign the relevant user permissions, and the Davis CoPilot button will appear in the Dock.

Start a new conversation with Davis CoPilot Chat by selecting it in the Dock or by pressing CTRL/CMD + I and entering your question.

New users can quickly get up to speed with Dynatrace by asking Davis CoPilot for help with basic commands, setup instructions, and troubleshooting tips. This reduces the learning curve and enables new users to become productive faster. The conversational interface provides step-by-step guidance, making the onboarding process smoother and more efficient.

If you’re already familiar with Dynatrace, you can rely on Davis CoPilot to provide detailed explanations for a wide range of expert questions related to exploring new use cases, advanced configuration topics, and building custom apps.

Here are some examples of questions you can ask Davis CoPilot:

• Onboarding: How do we start sending OpenTelemetry data to Dynatrace?
• Understanding Dynatrace: What is the difference between an event and a problem in Dynatrace?
• Exploring Dynatrace solutions: How can we comply with the Digital Operational Resilience Act (DORA) using Dynatrace?
• Configuring your environment: How do I set up an alert based on an anomaly detector?
• Developing custom apps: How can I import external table data and visualize it using the Dynatrace App Toolkit?

Get contextual assistance at the press of a button

Davis CoPilot seamlessly integrates into our use-case-specific Dynatrace Apps, offering you contextual insights and guidance at the press of a button. While we plan to release additional contextual app integrations in the coming months, several will be available a few weeks after launch, allowing Davis CoPilot to provide you with insights into:

• Kubernetes warning signals
• Individual problem details and the relationships between problems
• Database performance optimization

Simplify Kubernetes: Davis CoPilot decodes warning signals

Understanding the background and root cause of warnings often requires in-depth subject matter expertise. That’s why we integrated Davis CoPilot into Kubernetes. Instead of manually looking up error messages, Davis CoPilot translates warning signals into clear, understandable language. In addition, Davis CoPilot offers a list of typical root causes and related remediation steps. This way, newcomers can quickly become proficient, and experts can elevate their expertise to hero status.

Problems demystified: Davis CoPilot provides insights into root causes

In Problems, Davis CoPilot provides clear summaries of problems, their root causes, and the suggested remediation steps. Davis CoPilot explains individual issues in clear language from the problem details page and can perform a comparative analysis when multiple problems are selected from the list view. This helps you identify common root causes and propose corrective steps without relying on a team of experts and waiting for hours for critical insights. If you want to learn more, have a look at Wolfgang Beer’s latest blog post and learn more about recent advancements in the Problems app.

Optimize database performance: Understand query execution plans

Query execution plans provide detailed information on how a database will execute an SQL query. While these provide the raw data on how to improve query performance and reduce resource consumption, they require expert knowledge to read and interpret. Now, in Databases, Davis CoPilot can provide natural language explanations of execution plans, breakdowns of relevant details, and recommendations on how to improve statement performance. This gives non-expert database users, such as developers, the knowledge they need to optimize their application performance and database utilization.

Empower your teams with Davis CoPilot today

The launch of Davis CoPilot Chat marks the second milestone of our journey. We’re committed to continuously enhancing the assistant’s capabilities with upcoming features, including query explanations, workflow actions, and troubleshooting guides.

Get started with Davis CoPilot today and transform how you and your teams interact with Dynatrace:

• Try Davis CoPilot Chat on the Dynatrace Playground
• Activate Davis CoPilot in your environment and grant your users access.

Thanks for joining us on this exciting journey. We look forward to your feedback and to seeing how Davis CoPilot helps your teams achieve their goals.

Davis CoPilot Chat, as well as the Dynatrace Apps integrations mentioned in this blog post, will be available starting with the release of Dynatrace SaaS version 1.307.

The post Davis CoPilot expands: Get answers and insights across the Dynatrace platform appeared first on Dynatrace news.

AI, especially AIOps, has emerged as a pivotal solution, promising to avoid downtime. The 2024 State of AI Report highlights this trend, with 89% of technology leaders anticipating that AI will significantly enhance incident response by learning to automate and optimize various tasks, such as performance monitoring and workload scheduling.

AIOps can identify and address potential issues before they become major incidents by learning from history and analyzing large amounts of data in real time. This approach improves operational efficiency and resilience, though it’s not without flaws. The complexity of IT environments and the changing nature of threats necessitate human oversight and ongoing adjustment of AIOps systems to handle unforeseen challenges and ensure optimal performance. Additionally, predictions based on historical data are reactive, solely relying on past information to anticipate future events, and can’t prevent all new or emerging issues. This limitation highlights the importance of continuous innovation and adaptation in IT operations and AIOps strategies.

“The shift from reactive to preventive operations represents the next evolution in AIOps.”
Bernd Greifeneder, CTO Dynatrace

When Dynatrace set out with Davis® AI over 10 years ago, pioneering AI-driven operations, we focused initially on problem identification before moving on to problem remediation. The next milestone in enhancing the capabilities of Davis AI—another pioneering step forward in AI-driven operations—is outright problem prevention. In this blog post, we explain how the unique combination of causal, predictive, and generative AI—augmented by the latest Davis AI advancements—is transforming how Dynatrace customers manage and optimize their IT infrastructure.

Automatic root cause detection

Modern, complex, and distributed environments generate a substantial number of events. This necessitates additional requirements such as minimizing the total number of issues, eliminating false positives, and conducting accurate root cause analysis.

Dynatrace has a longstanding reputation for accurately analyzing root causes and identifying related events. While other methods typically rely on mere correlation and historical data analysis, we’ve further enhanced our capabilities by implementing causational analysis, which leverages contextual information automatically gathered during data ingestion and processing in addition to historical data analysis. This is achieved using Dynatrace Grail™, our causational data lakehouse, which unifies all data in an always-up-to-date topology model. By applying causal AI to incoming data in real time, Davis instantly learns and continuously adapts to new information. This facilitates more precise root cause analysis and anomaly detection, including identifying seasonal anomalies and establishing auto-adaptive thresholds.

When applying this Davis root cause detection within our own IT environment, Davis effectively filters out over 99.9% of incoming data noise, condensing hundreds of thousands of daily system events into no more than four or five incidents that require attention from our IT operations team.

These algorithms are not limited to monitoring IT environments. At our February 2025 Dynatrace Perform session on exploratory analytics with AI-driven insights, the Performance Engineering Lead of XXXLutz—one of the world’s largest furniture retailers operating more than 370 stores across Europe—explains how XXXLutz utilizes Davis AI to proactively identify critical order drops, allowing them to respond quickly and effectively to changing market conditions and ensuring that their business remains agile and responsive to the needs of their customers.

Problem journey and reactive remediation

At the core of Dynatrace problem remediation stands the Problems app—an optimized view into opinionated insights, details, and context of each detected issue—for Operations, SREs, and developers. It filters billions of log lines, including the topology of each incident and its affected entities, for efficient problem triaging and troubleshooting, resulting in a 56% faster mean time to repair (MTTR) for critical incidents.

With the latest release, we drive this further by improving the automatic connection of relevant log and trace data for further drill down, presenting the full context of an issue in a single view. This provides comprehensive visibility into even complex architectures, simplifying the process of examining relevant details and addressing code-level issues, reducing 100 clicks and manual filtering to a single click with no loss of context.

By utilizing Davis CoPilot™, you can conduct comparative analyses of multiple issues, obtain natural language summaries of individual problems, and receive contextual recommendations along with specific remediation steps.

You can also link troubleshooting guides created in Notebooks to remediated issues, thereby building an intelligent knowledge base. Davis automatically connects additional documents as well as stored workflows. So the next time a similar problem arises, Davis brings up related guides, enabling teams to learn from previous experiences and reducing the risk of knowledge loss.

Please refer to our recent blog posts for more information on utilizing Problems for AI-driven insights and the latest Davis CoPilot advancements.

Automating the remediation

While obtaining comprehensive insights is beneficial, true transformation occurs through the use of tools that automatically execute remediation steps. To implement these “AI-driven operations,” it’s essential to forecast future requirements, including capacity demands, potential system failures, and security incidents.

Traditional forecasting engines typically depend on historical data, stored in metrics. In contrast, Davis AI generates real-time predictions, facilitating proactive operations. This capability is due to Davis’s ability to process raw data, such as logs, for forecasting, leveraging Grail to execute previously unattainable queries.

Consider the following scenario: You begin by retrieving and analyzing logs to identify relevant values for automation. Once this task is complete, you proceed to your pipelining tool to configure ingestion rules that extract these values into metrics and then wait several weeks for your prediction engine to generate alerts that can serve as triggers for your workflows.

However, when utilizing Dynatrace with its integrated anomaly detection and forecasting capabilities, you gain the advantage of schema-less data analysis and the ability to process any raw data into time series in real time. This significantly reduces the time required to establish AIOps workflows from several weeks to less than 30 minutes.

Preventive operations

The complexity of modern software environments makes it challenging to determine a service’s reliability solely through testing. It’s impractical to emulate scenarios such as generating a million tickets to assess performance capabilities. This necessitates real-time insights and operations rather than reactive problem-solving or raising alerts to notify personnel.

Preventive operations address this need by enabling proactive corrective actions before issues arise, akin to predictive maintenance. AI-supported anomaly detection identifies parameters that deviate from the norm, allowing for automatic configuration adjustment to mitigate potential problems preemptively.

Davis CoPilot combines the “power of three”:

• Davis causal AI for identifying anomalies and root cause analysis
• Davis predictive AI for precise forecasting and determining when to take action
• Generative AI capabilities that perform actions beyond simply sending notifications or restarting services

In this way, Dynatrace extends AIOps beyond traditional IT operations tasks and addresses complex scenarios, including security use cases such as threat observability. Consider the following real-world example:

At Dynatrace, we log all failed login attempts. We can predict potential threats when abnormal patterns are identified and raise a security event by utilizing seasonal baselining. The subsequent workflow involves checking the IP address and generating a threat score. Upon reaching a certain threshold, a new ruleset is automatically added to the web application firewall. This entire process is fully automated, running before a problem even occurs, significantly reducing the response time from over an hour to a fraction of a second.

In another instance, automatic log pattern analysis crawling our application logs decreased the number of bugs in the production environment by 15% and freed up time previously spent on log analysis and triaging (in pre-prod), equivalent to 17 full-time employees. Consequently, these 17 developers can now dedicate their efforts to adding more value to Dynatrace.

Summary

The State of AI report states that over 88% of technology leaders anticipate AI will enhance incident responses and improve their teams’ ability to predict and proactively resolve service-affecting issues.

With Dynatrace, organizations are prepared to evolve their ITOps and SRE departments from troubleshooting to prevention, getting proactive with forecasting, and utilizing generative AI instead of purely focusing on history-focused root cause analysis.

Start your preventive operations journey with smart automation and auto-remediation that prevents larger issues.

Are you interested in gaining more insights?

• Read about recent advancements in our Problems app
• Get insights into Davis root cause analysis
• Learn more about how Davis CoPilot helps you be more productive
• Or dig deeper into how to utilize Davis forecasting capabilities within Dashboards

The post Advancing AIOps: Preventive operations powered by Davis AI appeared first on Dynatrace news.

When we launched the new Dynatrace experience, we introduced major updates to the platform, including Grail™, our innovative data lakehouse unifying observability, security, and business data, and Dynatrace Query Language (DQL) for accessing and exploring unified data. While Grail and DQL opened up nearly limitless possibilities for data exploration, mastering DQL was necessary to fully leverage the power of Grail. Our latest enhancements to the Dynatrace Dashboards and Notebooks apps make learning DQL optional in your day-to-day work, speeding up your troubleshooting and optimization tasks.

In this blog post, we look at these enhancements, exploring methods for monitoring your Kubernetes environment and showcasing how modern dashboards can transform your data. Furthermore, we illustrate how these methods work seamlessly with Dashboards and Notebooks to enhance their effectiveness.

The many paths to building a dashboard or notebook

Getting started with the new Dashboards is now easier than ever, offering unprecedented ease and capabilities for exploring your data. We’ve not only improved how you interact with data in dashboards and notebooks, we also enhanced the way that underlying data can be shared across apps. These updates expand your options for exploration and creation, helping you to build your dashboards and notebooks quicker and more intuitively.

You can now:

• Intuitive data access with Davis CoPilot AI assistant
• Kickstart your creation journey using ready-made dashboards
• Accelerate your data exploration with seamless integration between apps
• Start from scratch with the new Explore interface
• Search for known metrics from anywhere

Let’s look at each of these paths through an end-to-end use case focused on Kubernetes monitoring.

Kickstart your creation journey using ready-made dashboards and notebooks

Creating dashboards and notebooks from scratch can take time, particularly when figuring out available data and how to best use it. Ready-made dashboards and notebooks address this concern by offering pre-configured data visualizations and filters designed for common scenarios like troubleshooting and optimization.

These ready-made dashboards offer your platform engineers, who oversee Kubernetes environments, immediate and comprehensive data visibility. This allows platform engineers to focus on high-value tasks like resolving issues and optimizing performance rather than spending time on data discovery and exploration.

Kickstarting the dashboard creation process is, however, just one advantage of ready-made dashboards. Let’s assume you’re already using the new Kubernetes app, which offers a comprehensive overview of your Kubernetes environments and their telemetry. There are cases where more flexible data presentation is needed. Our new ready-made dashboards for Kubernetes not only provide instant insights into your clusters, nodes, workloads, or pods but also enable you to extend and customize the data shown in the Kubernetes app, leveraging the context-rich data from Dynatrace Grail. So, for example, if you need to seamlessly integrate metrics with logs for your workloads, you can create a customized view based on the pre-configured dashboard that consolidates all critical signals in one place, which is particularly essential for troubleshooting.

Finding ready-made dashboards is straightforward. Navigate to the list of dashboards and set the filter at the top left to Ready-made. Select the title of any dashboard that interests you, or use the search bar to narrow down the results.

Accelerate data exploration with seamless integration between apps

In developing the new Dynatrace experience, our goal was to integrate apps seamlessly by sharing the context when navigating between them (known as “intent”), much like sharing a photo from your smartphone to social media. This approach acknowledges that in any organization, software doesn’t work in isolation; boundaries and responsibilities are often blurred. This is even more true for critical scenarios like troubleshooting, which often requires more than the capabilities of a single person or app.

Let’s make this more tangible by using the Kubernetes cluster dashboard and demonstrating how this concept helps you to:

• Seamlessly navigate between apps while maintaining context.
• Effortlessly explore data in Dynatrace and create dashboards from it.

When working with the Kubernetes cluster dashboard, you have two options for digging deeper into further analysis, both using the Kubernetes app. You can use dynamic markdown links, which include the values of the actual dashboard variables, or you can utilize the open-with feature (the “intent” concept), which uses the actual context of the dashboard tile you’re viewing. With this latter approach, you even have the choice of passing a single value (Open field with) or all underlying data (Open record with) for the respective element (row, series, cells, etc.) when navigating to another app.

Next, let’s use the Kubernetes app to investigate more metrics. The intent concept and the open with feature can also be applied in reverse to include data or specific visualizations from an app on a particular dashboard. An example of this is shown in the video above, where we incorporated network-related metrics into the Kubernetes cluster dashboard.

Start from scratch with the new Explore interface for metrics in Dashboards and Notebooks

Once you’ve learned how to monitor your Kubernetes cluster using a ready-made dashboard and extending it with context from other apps, the next step is understanding how to create and extend such dashboards using the Dashboards or Notebooks app.

Exploring and adding metrics from scratch

Let’s revisit our example from the last chapter and add the same Kubernetes network metrics, this time by using the new Explore metric interface that allows you to:

• Browse and add multiple metrics to a single tile
• Apply basic commands such as aggregation, filter, and split

• Use expressions to do calculations based on previously added metrics

The revised Explore interface, as shown in the clip above, now includes logs, metrics, events and business events, offering an improved filtering experience that enables you to:

• Type ahead to add, edit, or remove available filters
• Control how filters are applied via a rich set of operators (=, !=, in, not in, >=, <=, >, <)
• Place wildcards before and after your filter values to automatically generate the best matching DQL when using startsWith, endsWith, or contains.
• Control how filters are combined with logical operators, such as AND or OR
• Easily filter entities by ID, name, or tags in the web UI
• Get suggestions for metric values and entities (IDs, names, tags) for all data types

Blend metrics with data from Explore logs for a more comprehensive view to start log analysis

With the enhanced Explore Logs interface, retrieving and viewing logs from your Kubernetes workloads is straightforward. By incorporating a new tile, you can integrate these logs into your dashboard along with key metrics, such as the new Kubernetes network metrics we added earlier.

Leverage dashboards to monitor your environment in real time through log data. Once you identify an anomaly that requires your attention, you can start troubleshooting by delving further into the issue using the Open with option and the intent mechanism in the new Logs app. This app provides advanced analytics, such as highlighting related surrounding traces and pinpointing the root cause, as illustrated in the example below.

Leveraging the capabilities of Grail and Smartscape® topology, Dynatrace seamlessly integrates logs, metrics, and traces to offer enhanced context for troubleshooting and in-depth analysis. This integration facilitates a comprehensive understanding of individual transactions through the Distributed Traces app and aids in pinpointing the root cause of issues when using the Problems app.

Intuitive data access with Davis CoPilot AI assistant

There’s also a brand new and completely different option for analyzing data using natural language; using the power of generative AI, Davis CoPilot™ converts your conversational prompts into accurate DQL commands, allowing both non-technical users as well as experienced data analysts to make data-driven decisions faster than ever before.

To learn more about how Davis CoPilot empowers you and your teams, see our blog post, Announcing General Availability of Davis CoPilot: Your new AI assistant.

Search metrics from anywhere

A speedy way to begin your data exploration journey, particularly if you already know which data you need, is to utilize our global search feature to effortlessly find and explore metrics from anywhere on the Dynatrace platform. This feature lets you explore any available metric and add it to Notebooks or Dashboards.

Imagine a colleague mentioning a newly released metric for Kubernetes during a coffee break. Rather than manually exploring the Kubernetes app you can simply open the Dynatrace global search and enter “Kubernetes network.” The relevant metrics are then immediately displayed alongside further details.

This efficient method allows you to easily browse and identify the appropriate metrics; adding them to your notebooks and dashboards requires just a single click.

Get started discovering and exploring your data

It has never been easier to analyze data within Dynatrace. Kickstart your data exploration journey and familiarize yourself with ready-made dashboards and the new Explore data interface. By the way, we also added new data visualization capabilities by adding new chart types and chart interactions.

Curious about our latest releases and upcoming features for Dashboards and Notebooks? Check out our community roadmap thread to stay updated!

The post Transform data into insights with Dynatrace Dashboards and Notebooks appeared first on Dynatrace news.

Deal with data overload in the enterprise

In today’s rapidly evolving digital landscape, enterprises are inundated with vast amounts of data. Extracting meaningful insights from this data is crucial for staying competitive. However, traditional data analysis techniques can be time-consuming and demand specialized expertise, limiting how quickly and easily insights can be obtained.

Empower deep data analysis with natural language queries

Davis CoPilot enhances efficiency and productivity by seamlessly integrating generative AI throughout the Dynatrace platform. This feature allows you to effortlessly gain insights and generate queries without needing to learn new syntax or manage complex commands. Consequently, Dynatrace becomes accessible to a broader audience, including non-technical users and those who don’t work with Dynatrace on a daily basis, and empowers teams to make faster data-driven decisions.

Empower users with intuitive data access—without compromising security

At Dynatrace, we recognize the complexities associated with data environments. DQL, the query language employed to analyze data stored in Dynatrace Grail™ data lakehouse, offers remarkable versatility and power, serving as an essential tool for experienced users seeking to fully harness Grail’s capabilities. Davis CoPilot simplifies the data querying process for both professionals and beginners by enabling interactions through natural language. This democratizes data access, allowing all users to generate valuable insights swiftly and effortlessly. Consequently, the data analysis process is accelerated, empowering teams to make informed, data-driven decisions with increased speed and precision.

At Dynatrace, we prioritize the protection of your data. Our solutions are engineered to be secure, reliable, and entirely transparent. Davis CoPilot guarantees that your confidential information is never at risk of being leaked or disclosed across environments, as we ensure continuous protection of your prompts and data. Furthermore, there is no automatic model training or fine-tuning based on your usage, ensuring that your data is employed strictly for its intended purpose—to generate DQL and provide swift insights. This steadfast dedication to security and transparency enables you to use our tools confidently, trusting that your data is well-protected. Look at our documentation to get more insights into the privacy and security aspects of Davis CoPilot.

Get started with quick analysis in Notebooks and Dashboards

Davis CoPilot allows you to perform rapid data analysis in Notebooks and Dashboards by translating natural language prompts into Dynatrace Query Language (DQL). The results are automatically executed and returned, making complex data analysis more accessible than ever before.

Simply create a new notebook or dashboard, then select + Add > Davis CoPilot. Enter your prompt (or try one of our suggestions), and select Run. Davis CoPilot will generate and auto-execute the DQL so you can go from question to data insights in seconds. If you’d rather refine your query before executing it, open the dropdown list next to the run button and select Generate DQL only (this feature is currently only available in Notebooks).

Environment-aware queries unlock full data-context awareness

Davis CoPilot is much more than an AI tool that helps you create queries. Davis CoPilot knows the context of your data, which results in more precise answers using a feature called environment-aware queries.

Having environment-aware queries configured allows Davis CoPilot to identify unique data fields and custom metrics in your environment. You can now run more complex analyses and get better results by crafting more accurate queries that identify and reference relevant entities, events, spans, and metrics straight from your environment. And, of course, we do this without putting you or your data at risk. This functionality is opt-in, and you have full control over which data tables and buckets are accessible to Davis CoPilot. Let’s look at some examples:

If you’re an application owner tracking travel bookings for new trips on a travel website, you’ll likely need to track:

• profit made on each booking  (as a business event)
• applicable discounts (as a business event)
• length of time it takes customers to complete a booking (as a custom metric)

With this in mind, you might give Davis CoPilot the following command: “Show me the average revenue and price reduction for new trips over the last month.”

If you have environment-aware queries configured, the following DQL will be generated automatically, and you’ll get the relevant results you’re looking for.

fetch bizevents , from:now() – 30d 
| filter event.type == “new trip” 
| makeTimeseries interval:1h, {profit= avg(profit), discount= avg(discount)

With environment-aware queries configured, Davis CoPilot infers that “revenue” refers to the profit field and “price reduction” refers to the discount field, even though your prompt doesn’t use the correct field names. However, if you don’t have environment-aware queries configured, Davis CoPilot can’t identify all relevant fields. For example, the following incorrect DQL will be generated if the same conversational command is issued when environment-aware queries are not configured. In such cases, you won’t get any results since the fields mentioned in the command don’t exist in your environment.

fetch bizevents, from:now() – 30d 
| filter event.type ==  “new trip”
| makeTimeseries interval:1h, {avg_revenue = avg(revenue), 
  avg_price_reduction = avg(price_reduction)

Alternatively, you might ask Davis CoPilot the following: “On average, how long does it take customers to book new trips?” If you have environment-aware queries enabled, the following DQL will be generated, and you’ll get the relevant results you need.

timeseries avg(new_trip_booking_duration)

Conversely, if you don’t have environment-aware queries configured, you’ll likely receive an error message because Davis CoPilot can’t correctly map your question to your custom metric key. In this case, Davis CoPilot can’t generate a valid DQL query since it won’t be able to find a matching built-in metric.

User permissions are enforced both with and without environment-aware queries, ensuring that Davis CoPilot provides relevant responses that comply with individual data-access rights. Environment-aware queries truly unlock the power of Grail for everyone in your organization.

What’s next for Davis CoPilot

This is just the beginning of our new AI assistant journey. We’re committed to making Davis CoPilot even better, and we’ve got some fantastic features coming your way, from query explanations to problem insights, document generators, and more.

We value your feedback and are continuously working to enhance our product. Want to share your thoughts? You can share your learnings directly from the Davis CoPilot interface. Your feedback helps us refine the functionality and better meet your needs. You can also request to participate in ongoing or upcoming Preview programs. Get in touch with your Dynatrace account manager if you’re interested.

Get started today and embrace the future of data analytics

The launch of Davis CoPilot marks a significant advancement in data analysis capabilities. If you have a Dynatrace Platform Subscription, Davis CoPilot is available for you with the release of Dynatrace SaaS version 1.301. If you have a classic license, Davis CoPilot is available for you with the release of Dynatrace SaaS version 1.304.

Empower your team with the ability to effortlessly transform natural language prompts into actionable insights. Activate Davis CoPilot in your Dynatrace environment today and explore how it can transform your data analysis workflows.

For more information and to get started, please visit our documentation. Thank you for being part of this exciting journey with us. We look forward to your feedback and seeing how Davis CoPilot helps you achieve your goals.

The post Announcing General Availability of Davis CoPilot: Your new AI assistant appeared first on Dynatrace news.

In a digital-first world, site reliability engineers and IT data analysts face numerous challenges with data quality and reliability in their quest for cloud control. Increasingly, organizations seek to address these problems using AI techniques as part of their exploratory data analytics practices.

Exploratory data analytics is an analysis method that uses visualizations, including graphs and charts, to help IT teams investigate emerging data trends and circumvent issues, such as unexpected traffic spikes or performance degradations.

Challenges to exploratory data analytics

Among the challenges analysts face are multiple heterogeneous data sources, noisy or incomplete data, insufficient causal reasoning (faulty connections between event cause and effect), and untrustworthy AI, according to an article from the Columbia University Data Science Institute.

Another hurdle is mistaking easy patterns as effective analysis, according to an article in the Harvard Data Science Review. Techniques analysts use to emphasize patterns, such as aggregating data by default, can cause them to overlook variation and uncertainty in their data, so they can draw conclusions that the data don’t fully support.

AI techniques provide a solution

A first line of attack is selecting the right analytics tool, which can help teams detect meaningful patterns in real-time data, integrate data from multiple sources, and render data visualizations.

To that end, in 2022, Dynatrace released Grail, the auto-indexing, schema-on-read data lakehouse, along with Notebooks and Dashboards. This expansion of the Dynatrace platform builds on its foundation, which uses topology mapping and causal AI, an AI technique based on fault-tree analysis, to pinpoint root causes.

The next challenge is harnessing additional AI techniques to make exploratory data analytics even easier. Dynatrace Grail, Notebooks, Dashboards, and multiple AI techniques combine to provide analysts with instant insights, as demonstrated by Thomas Ziegelbecker, a senior product manager at Dynatrace, and his colleagues Peter Zahrer, principal product manager, and Gabriele Hasson-Birkenmayer, senior product manager at the recent Perform 2024 conference.

Three steps in exploratory data analytics: Discover, browse, explore

Grail captures heterogeneous data from across the network in one place while retaining its context and semantic details, which eliminates the limitations of traditional databases. From this unified, semantically rich data resource, analysts can explore data on the fly and share findings using Notebooks and Dashboards.

With Notebooks, analysts can “explore their data, use ad-hoc analysis, and work with data in a sequential fashion to refine, refine, refine,” Ziegelbecker said. “With Dashboards, you can observe [the data you’re interested in] over time,” covering common monitoring use cases such as system health.

“When you work with data, it comes down to three steps: Discover, browse, and explore,” Zielgelbecker said. “Start by asking yourself what’s there, whether it’s logs, metrics, or traces. Once you double down on the type, you want to figure out, or browse, which metric is relevant. Then when you have the metric, you want to explore it—what splits are available, how can I break it down, how can I aggregate it, and so on.”

Discover data using global search on the new ‘explore’ section and tile type in Notebooks and Dashboards

From a dashboard, an analyst can investigate an issue, such as a 25% error rate from a key Kubernetes cluster. According to Ziegelbecker, the discovery phase of exploratory data analytics can start in two ways: doing a global search or using the “explore data” interface.

1. Discovery using global search. Analysts can easily navigate to any entities (hosts, applications, processes, Kubernetes nodes, and so on) or metrics in their environment using global search. Users can trigger the global search from any context with CTRL/CMD +K. Type > to see a list of all available search categories. Select the relevant category (such as “Metrics”) and type the search term. This provides a quick way to navigate data or start a DQL query.
2. Discovery using the “explore data” interface. For those who think visually, Dynatrace provides an interface to explore data within Dashboards and Notebooks. Using step navigation and drop-down menus, this simplified UI approach streamlines query building for essential tasks such as filtering, aggregating, and sorting. This method also enables users to aggregate, filter, sort, limit, or split metrics.

Browse data: Advanced exploration using DQL

Once analysts discover the data they’re interested in, the next step is to refine the search and share results.

Dynatrace Notebooks is an interactive data exploration interface that enables users to collaborate using code, text, and rich media to build, evaluate, and share insights.

“[Notebooks] is purposely built to focus on data analytics,” Zahrer said. “We use Dashboards to monitor and present; we use Notebooks to work with the data and, at the same time, document what we just did.”

Using DQL, users can query any data stored in Grail, such as metrics, logs, events, and time series, in context of any entity (hosts, processes, applications) in the monitored environment, provided by Dynatrace Smartscape.

Using Notebooks, an analyst can extend the query started in the discovery phase to further troubleshoot an issue, such as a 25% error rate in a Kubernetes cluster. “I’m interested in seeing whether the [Kubernetes] log errors we’re seeing on our dashboard are related to a misconfiguration on the Kubernetes side,” Zahrer said.

To relate logs and metrics using Grail, analysts can use DQL within the Notebooks app to further explore the problematic Kubernetes logs. DQL prompts help analysts filter on enriched data from Dynatrace OneAgent—a single file that automatically discovers all the processes running on a host and auto-instruments application pages.

By enriching data with the topological context of Smartscape through OneAgent and keeping data consistent, Zahrer said, Dynatrace helps analysts do causation-based cross-correlation to see how events relate and to pass on details, such as Kubernetes nodes involved in errors, to further refine investigation and analysis.

Explore data using Davis CoPilot—a generative AI technique for advanced analytics of logs and metrics

As an alternative to identifying and exploring data, analysts can also use Davis CoPilot to achieve the same result.

“Davis CoPilot is a generative AI that works in concert with our predictive AI and with our causal AI,” Hasson-Birkenmayer said. The blend of these three AI techniques—predictive, causal, and generative AI—make up the Davis composite, or hypermodal, AI approach.

Davis CoPilot generative AI helps analysts get started with—and get more proficient using—DQL. Instead of configuring tiles or sections, or instead of writing DQL, analysts can explore data with a natural language prompt. For example, “you can ask Davis CoPilot to ‘summarize all logs by status,’” she explained. “In the background, Davis converts the prompt into DQL and auto-executes it. So you can go straight from a conversational prompt using your natural language directly into insights.”

Because the Davis CoPilot integration in Notebooks is a DQL assistant, it runs the query so analysts can see if the results are what they intended without having to first review and validate the DQL syntax themselves. If users need to refine the results, they can do so either by refining the natural language input or by creating a new DQL section and refining the details directly.

Analysts can also customize data visualizations according to their needs. “In some cases, I am actually choosing and selecting a particular visualization, and in some cases [such as with certain metrics], it gets done automatically,” Hasson-Birkenmayer said.

Exploratory data analytics enhanced by AI techniques

Starting from a dashboard or notebook using the “Explore data” interface to cover simple data exploration routines, analysts can advance their inquiries in Grail using DQL. With a combination of AI techniques—generative AI using data verified by predictive and causal AI—Davis CoPilot enables analysts to further refine complex explorations using natural language queries.

The post AI techniques enhance and accelerate exploratory data analytics appeared first on Dynatrace news.

Hypermodal AI, which combines predictive AI, causal AI, and generative AI, boosts productivity across operations, security, development, and business teams.

This expansion of Davis AI complements the proven Dynatrace predictive AI model (for example, forecasting and anomalies) and our causal AI model (for example, determination of a problem’s root cause, security risks, user impact, and steering automation), which are at the core of the Dynatrace platform.

Davis CoPilot empowers users to effortlessly create queries, data dashboards, and data notebooks using natural language and provides coding suggestions for workflow automation, reflecting the unique attributes of each customer’s hybrid and multicloud ecosystem. It also simplifies and accelerates onboarding, configuration, and adoption of the Dynatrace platform.

What is hypermodal AI, and why is it essential for reliable observability, security, and automation at scale?

Hypermodal AI intelligently combines multiple AI techniques—predictive AI, causal AI, and generative AI—helping organizations effectively solve BizDevSecOps use cases.

Dynatrace applies these techniques to the broadest set of modalities in the market, including the data types of metrics, traces, logs, behavior, topology, dependencies, events, and more, with unmatched precision for precise predictions, accurate determinations, and meaningful insights.

Davis AI transforms and augments data to enable more useful analysis, perform automatic tasks, and respond to user requests:

• Predictive AI uses machine learning (ML) and statistical methods to recommend future actions based on data from the past. Dynatrace uses the various data types across metrics, logs, traces, behavior, events, and more in its Grail™ data lakehouse and causal dependencies from Dynatrace Smartscape® to provide continuous forecasting and anomaly prediction, including cloud application health, infrastructure needs, sales, and customer experience trends, seasonality, and other historical behaviors.
• Causal AI processes observability, security, and business data in the context of causal dependencies from Dynatrace Smartscape topology to precisely determine the needle in the haystack in continuously and dynamically updated software services. It groups anomalies, pinpoints root causes, ranks security risks, enables precise attack investigation, and provides business impact assessments, all automatically. This AI also triggers automated remediation actions. It further enables teams to explore trends or patterns with built-in domain and topology context.
• Generative AI drives productivity through AI-powered analytics and automation for all members of your organization. Davis CoPilot interprets natural language to create queries, dashboards, and notebooks and provides suggested code for automation workflows. It further simplifies access to best practices for observability and security use cases and answers “how-to” questions precisely. It also guides users who want to observe new technologies or apply advanced configurations.

The combination of AI techniques is vital for observability and security use cases

Generative AI is a transformative technology for delivering productivity gains. Observability, security, and business use cases raise additional challenges as they need precision and reproducibility.

Large language models (LLMs), which are the foundation of generative AIs, are neural networks: they learn, summarize, and generate content based on training data. When a user asks a question, generative AIs create an answer word by word. They predict the probability of the next word or sequence of words given the input prompt. They employ probabilistic sampling techniques and allow controlled randomness to diversify responses.

This means that the same prompt/question will provide different responses. Because this randomized, probabilistic approach is not rooted in precise causal data, a pure generative AI approach renders use cases that require precision impossible.

Davis AI combines AI techniques for precise and reliable outcomes:

• Predictive AI and causal AI provide context to Davis CoPilot. They automatically enrich prompts with specific information, which provides better recommendations and precise, reproducible results.
• Davis CoPilot generative AI doesn’t only react to user inputs. It can also be triggered automatically by predictive AI or causal AI events (for example, to recommend remediation actions automatically).

Hypermodal AI unleashes exponential value: Step-by-step example

In this example, a user builds a Dynatrace dashboard for all business-critical services that will be at risk during Black Friday. The steps required to complete this task can be categorized as either predictive , causal , or generative .

 Understand the meaning of questions.

 Identify all user sessions that contain conversion metrics (using Smartscape).

 Identify all services that are needed for these user journeys (topology using Smartscape).

 Predict how these services will behave under a higher load based on historic data.

 Choose the services that are nearing their limits (topology metrics).

  Choose the services that caused problems in the past.

  Use input to generate a dashboard and queries.

 Determine if remediation workflows should be set up.

Dynatrace Davis^® AI provides answers and automation, and boosts productivity for multifaceted use cases

Automatic root cause analysis

Davis AI automatically detects user-facing issues and assesses their impact on the business and affected users. Then, Davis uses context—such as topology, transaction, and code-level information—to identify the precise root cause of problems.

Davis CoPilot can provide recommended actions to remediate issues.

Automatic root cause analysis enables AIOps (or AI for IT operations) automation using the Dynatrace AutomationEngine.

Davis AI with predictive AI and causal AI is generally available and used by all Dynatrace customers. Start your free trial now! Davis CoPilot™ will be available in 2024 as a core technology within the Dynatrace platform. Find more information and interesting links here.

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