AI is changing the way we work, from boosting our efficiency and executing tasks on our behalf. As AI enters the agentic era, Dynatrace Assist is your trusted partner for working smarter with Dynatrace. Powered by Dynatrace Intelligence, it helps you solve problems faster and brings AI into your daily workflow.

Today, we’re announcing Dynatrace Assist, our next-generation AI chat that goes far beyond answering questions. It lives where you work, understands your environment, and helps you get real work done with the full power of Dynatrace Intelligence behind every interaction.

Dynatrace Assist is the evolution of Davis CoPilot®, but make no mistake: this is much more than a rebranding. It’s a paradigm shift in capability, outcome, and value.

Your agentic partner helps you gather insights

Dynatrace Assist is where the full power of Dynatrace Intelligence, the new Dynatrace agentic operations system, is available at your fingertips. Instead of moving between dashboards, queries, and tools, you can simply start with the conversational interface: ask a question and let Dynatrace Intelligence do the work.

Dynatrace Intelligence interprets your prompt using generative, causal, and predictive AI capabilities to understand what you’re trying to achieve. Like a member of your team, Assist pulls together context from Grail, maps relationships utilizing Smartscape’s dependency graph, and collaborates in real time with Dynatrace agents. As the conversation unfolds, you move from understanding to exploration—following suggested drill-downs, refining your analysis, and understanding the next steps. This allows for a single, seamless flow from question to insight to execution.

Multi-step reasoning that understands your environment

Assist doesn’t just do what you ask—it thinks several steps ahead. It helps you understand the why, decide what to do, and see what comes next. And all of this is fully transparent, showing you exactly which data backs each decision, so you can trust the recommendations and proceed decisively and confidently.

Under the hood, Assist combines Grail’s unified data lakehouse with Smartscape’s dependency graph, layered with our causal and predictive AI. By leveraging the same tools that are hosted on the Dynatrace MCP Server, Assist can pull insights from any part of your environment and kick off deeper analysis when needed.

From incident to remediation in a single flow

AI on its own is meaningless. AI needs context and data to deliver real value, and you need intelligence that understands your environment and helps you move faster with confidence. Dynatrace Assist is built for exactly that. It works alongside you, sharpening decisions, removing friction from investigations, and accelerating the steps that typically slow teams down.

Let’s look at the following example: when a production issue arises, teams waste precious minutes jumping between dashboards, logs, and alerts to reconstruct what went wrong and determine the next steps.

Investigations start with a simple question like “Summarize all open problems and highlight those that need remediation.” Assist immediately pulls together the evidence. It connects Grail data, Smartscape relationships, and real-time signals from Dynatrace agents to piece together a clear explanation of the issue. Instead of scattered clues, Assist collaboratively guides you in selecting a problem to focus on and recommends how to remediate it. This continuous flow compresses investigation time, reduces MTTR, and gives teams a controlled way to move from insight to remediation without breaking focus.

Explain raw data with actionable insights

Logs, signals, and low‑level data points often require deep domain knowledge to interpret. In large digital environments, that knowledge is spread across different teams, which means understanding what a particular message means, how serious it is, and whether it requires action can easily slow teams down.

When you select a log entry (or another signal in Dynatrace) and ask Dynatrace Assist to explain it, it responds like an expert colleague with the necessary domain knowledge and an understanding of the system’s inner workings. It interprets the data in context, highlights what is important, clarifies the potential impact, and outlines likely causes or sensible next steps. Instead of searching for error codes elsewhere or waiting for someone with deeper expertise, teams get immediate clarity. This turns raw, technical signals into practical guidance and shortens the path from confusion to confident action.

Identify and respond to critical vulnerabilities

Vulnerabilities in your system significantly increase the risk of critical incidents, such as data loss or unauthorized access. When a new vulnerability alert appears, teams need immediate clarity about what it means, which services are exposed, and what actions they should take.

With a series of simple prompts, such as described below, Assist pulls in security findings, maps the impacted components across Smartscape, and identifies the root cause. It guides you through the decision-making process, including proposing a fix to the identified problem. All without leaving Dynatrace or switching between tabs, applications, or tools. This tight loop gives teams a faster understanding, more consistent responses, and a significantly reduced risk window.

Prompts used in this use case:

1. Are there any open threats I need to be aware of?
2. Investigate the SQL injection vulnerabilities and provide more details.
3. Show me input for <<add your vulnerability>>.
4. Give me a fixed query.

Dynatrace Assist is here, and it’s just the beginning

Dynatrace Assist doesn’t replace your expertise; it amplifies it, so you can resolve issues sooner, uncover insights more naturally, and move from intention to outcome with far less effort. It is your gateway to Dynatrace Intelligence, which makes you and your teams more productive, accelerates your operations, and uses the full Dynatrace platform to deliver real outcomes.

Ask. Analyze. Act.

And start today with Dynatrace Assist.

The post Dynatrace Assist: Ask, analyze, and act with Dynatrace Intelligence appeared first on Dynatrace news.

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.

Ninety-five percent of Generative AI projects fail to deliver measurable value, and leaders are under mounting pressure to demonstrate that their AI investments are effective. Achieving this requires clear visibility into how and where AI is used, and the outcomes it’s driving.

Dynatrace is setting a standard for observability across the AI stack, and we’re extending that same level of insight to our own AI tools. The new Davis CoPilot® Feature Adoption Dashboard utilizes the same telemetry that Dynatrace teams rely on to improve product quality. Assess effectiveness and optimize how Davis CoPilot supports productivity and decision-making, to move from experimentation to sustainable results.

Davis CoPilot, the Dynatrace platform’s LLM-powered assistant, helps teams work faster by leveraging the full context of their data on Dynatrace to deliver precise and actionable answers. The result is less time spent searching for information or onboarding users, and more time extracting the maximum value from the Dynatrace platform and achieving measurable outcomes.

IT and central team leaders typically offer Davis CoPilot to their users, with specific goals in mind that align with their organization’s broader AI strategy. Initiatives like these typically aim to achieve three key objectives:

• Adoption and engagement: Ensure AI becomes an integral part of routine workflows, so value can scale across teams.
• Productivity gains: Reduce manual effort, increase speed to insight, and improve the quality of outcomes.
• Demonstrable business value: Connect usage to measurable results, such as reduced operational costs, faster incident resolution, or improved service levels.

Understand how AI is used and how it delivers value

To make these objectives measurable, you need visibility into how AI is adopted by your users, the purposes it serves, and whether it delivers the intended value. Only then can you identify where improvements are needed. The ready-made Davis CoPilot Feature Adoption Dashboard delivers this visibility out of the box, showing how Dynatrace generative AI features are used across your organization. Based on the provided metrics and insights, administrators and central teams can make data-driven adjustments.

Know how frequently and for what purpose Davis CoPilot is used, in real time

Gain real-time visibility into when and how regularly teams are using Davis CoPilot in their workflows. The dashboard highlights active engagement, query activity, and usage trends across your organization, helping you understand where Davis CoPilot delivers the most value and where additional enablement may be needed.

By analyzing usage patterns, you can identify high-performing teams, monitor overall adoption progress, and ensure employees are using Davis CoPilot effectively to achieve meaningful outcomes.

For a deeper analysis, break Davis CoPilot usage down further by skill:

• Chat: Analyze chat interactions and workflow actions (currently in private preview), showing how users engage with Davis CoPilot to ask questions, troubleshoot issues, and automate routine tasks.
• Natural language querying: Tracks how users convert everyday language into Dynatrace Query Language (DQL) commands, supporting faster data exploration for both technical and non-technical users.
• Explain DQL queries: Shows how users rely on Davis CoPilot to interpret and summarize complex queries, making it easier to understand and build on existing work.
• Document search: Tracks how users engage with AI-driven document retrieval for accelerated troubleshooting in the Problems app.

Track user experience and satisfaction

To determine whether Davis CoPilot delivers value, it’s important to measure not only usage but also the quality of user interactions and outcomes. The dashboard tracks execution times and success rates to demonstrate how well Davis CoPilot performs in real-world scenarios. This makes it easier to identify technical issues such as invalid DQL generation or prompts blocked by guardrails and content filters.

Additional user feedback adds context to these signals. Thumbs-up and thumbs-down reactions help indicate where users achieve the desired outcome and where they run into problems. When negative feedback clusters around similar prompts or skills, administrators can examine the failed prompts, identify common failure modes, and understand the conditions that lead to them. This supports targeted follow-up actions, such as improving internal guidance for AI usage, reinforcing enablement for specific teams, and surfacing actionable improvement requests to Dynatrace.

For example, if multiple users struggle with natural language queries for Kubernetes data, admins can review the failed prompts, provide best practices, and verify that these measures lead to higher success rates over time. You can even consider enriching your data by adding common synonyms with OpenPipeline. Nequi shared their story at Perform 2025.

Together, operational metrics and contextual feedback help organizations to quickly identify friction points and take concrete steps to improve user outcomes and overall satisfaction.

Optimize performance of AI-generated insights

The dashboard also provides transparency into the queries executed through Davis CoPilot, including query counts and the volume of data scanned. This helps you better understand the resource and cost impact of AI-generated insights across your environment. This level of visibility is critical, as many AI initiatives stall because teams lack the insight to understand the operational impact of increased usage.

By identifying data-intensive queries early, you can optimize performance, control cost exposure, and avoid unexpected resource spikes that can undermine confidence in scaling AI. Capabilities such as segment filtering or organizing data into dedicated buckets allow you to fine-tune data access based on organizational needs. This gives you the ability not only to monitor AI activity but also to adjust and govern it responsibly, ensuring Davis CoPilot remains efficient, controlled, and aligned with your broader business objectives.

Make use of the full potential of Davis CoPilot

The Davis CoPilot Feature Adoption Dashboard equips you with the insights needed to scale Davis CoPilot responsibly, maximizing productivity gains while maintaining control. With clear visibility into usage, success rates, and operational impact, you can build a stronger foundation for continued AI expansion.

The dashboard is instantly available in the environments of Dynatrace customers who have enabled Davis CoPilot.

The post Enhance the impact of Dynatrace Davis CoPilot with built-in observability appeared first on Dynatrace news.

Dynatrace Query Language (DQL) delivers unlimited contextual analytics, but if you’re not writing queries every day, the learning curve can feel steep. Davis CoPilot® makes things easier by generating even complex DQL queries from natural language alone. With its latest enhancement, Davis CoPilot can also summarize and explain existing queries in context, showing what a query does, why it’s structured the way it is, and how the results relate to the underlying data. This helps teams validate intent, spot gaps, and confidently build on each other’s work without requiring deep query expertise.

Suppose you’ve opened a dashboard or notebook and found a complex query you didn’t write. You know the struggle: queries can be overwhelming to look at, key details may be nested or referenced elsewhere, and you might not be familiar with the specific data syntax or the user’s original intent. Even revisiting your own work after a few weeks can mean trying to remember what the dashboard was designed to show and how the query fits together. Reverse-engineering shouldn’t be a prerequisite for collaboration.

With the Summarize and explain queries Davis CoPilot skill, you get a clear, contextual explanation of any query, helping you quickly understand what it does.

Figure 1. Get an explanation of any DQL query. (video)

From creating queries to explaining them

Last year, we introduced natural language querying, allowing anyone to explore their data without learning DQL syntax. Now, Davis CoPilot can also interpret existing queries using the Dynatrace data model, explaining what the query does, how it filters and calculates results, and which data sources it uses. This reduces the effort required to work with complex syntax, facilitating the onboarding of new users while enabling experts to validate intent and iterate more efficiently.

The Explain and summarize queries skill is available in Notebooks and Dashboards. Review queries from teammates, tailor ready-made dashboards to your specific needs, and accelerate knowledge sharing.

Try it out on the Dynatrace Playground

Dynatrace offers a wide range of ready-made dashboards to help you get started instantly; however, sometimes you need to tailor dashboards to your unique use cases. With the Explain and summarize queries skill, you can instantly understand how the underlying queries were built by Dynatrace experts, giving you guidance and inspiration for your own customizations. See the examples below:

Log ingest overview dashboard: The table below highlights your noisiest log sources, helping you quickly pinpoint where excessive volume might be driving up ingest costs or masking real issues. With Davis CoPilot, you can see exactly how the underlying query is constructed, making it easy to extend the logic or use it as a template for your own ranking and cost-optimization dashboards.

^{Figure 2. Davis CoPilot explains a query of the top 20 log producers in your system.}

Databases overview dashboard: The following chart identifies slow or inefficient SQL statements that degrade application responsiveness, allowing you to focus your tuning efforts where they matter most. Use Davis CoPilot to break down the logic behind the analysis so you can adapt its scope, filter for critical services, or enrich results with additional business context.

^{Figure 3. Davis CoPilot explains a query that identifies the 20 most resource-intensive statements from Oracle databases.}

Kubernetes cluster dashboard: Optimizing Kubernetes requires clear visibility into how workloads consume cluster resources. This query returns CPU usage broken down by namespace, helping you detect saturation early and maintain efficiency. With Davis CoPilot, you can see exactly how the query works and then adapt it to create your own capacity-planning tool.

^{Figure 4. Davis CoPilot explains a query returning CPU quotas per Kubernetes namespace.}

Ready to try it out yourself?

This feature is already available in all environments; you just need to ensure that Davis CoPilot is turned on and that you have the necessary permissions for this skill. Learn more about Davis CoPilot summarization and explanation of DQL queries in Dynatrace Documentation.

The post Understand and validate DQL queries using Dynatrace Davis CoPilot 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.

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.