Dynatrace AI agents begin working for you on day one, and are built to grow with you

AI agents are everywhere in tech conversations right now, but what agents can you actually use today to make your job easier? In Dynatrace, ready-made agents help developers, SREs, and IT operations teams investigate issues, understand system behavior, and reduce manual work using the data they trust every day. Dynatrace ready-made agents are not concepts or previews; they're available now, integrated into existing Dynatrace workflows, and designed to solve real operational problems. For teams ready to go further, Dynatrace agents lay the groundwork for autonomous operations. This blog shows what Dynatrace ready-made agents are, how to get value from them quickly, and how to decide which agents are relevant for you, using concrete examples rather than promises.

From generic AI to task-focused operational agents

Dynatrace ready-made agents are purpose-built capabilities that apply Dynatrace intelligence to specific, recurring operational tasks. Each agent focuses on a clearly defined problem, such as explaining why a service is slow, summarizing unusual behavior in an environment, or helping you understand what changed and why it matters. These agents are designed to take a question or a signal based on the exact data that is in your environment and organization and turn it into a useful answer you can act on.

Because Dynatrace agents are ready-made, there is no need to define prompts, train models, or design behavior from scratch. Each agent already knows:

• What type of input to expect,
• Which Dynatrace signals and context it should use,
• And what output types are most useful for each addressed problem type.

All available ready-made Dynatrace agents can be found in Dynatrace Hub.

Trigger agent actions with Dynatrace Workflows and the Dynatrace MCP Server

Ready-made agents can be triggered automatically as part of Dynatrace Workflows or available wherever you already work via the Dynatrace MCP Server.

Using agents in Dynatrace Workflows

Dynatrace Workflows lets you run agents in response to events or on a schedule. Instead of manually asking questions about potential problems and remediation steps, the workflow autonomously responds to changes in your environment.

For example, the Kubernetes Troubleshooting Agent runs nine parallel queries for data enrichment, and Dynatrace Intelligence turns all the information into a structured diagnosis. Customize the agents to your needs, including instructions for human approval steps and automated remediation.

Figure 1. Dynatrace Kubernetes Troubleshooting Agent in action.

The fastest way to get started is with Dynatrace ready-made agentic workflow templates, currently available in a preview release. Instead of building from scratch, you get proven automations that summarize issues, suggest remediation, and deliver insights directly to the tools your teams already use.

Figure 2. Agentic workflow templates available in preview

Power users can go further by building their own agentic workflows that combine Dynatrace Intelligence actions with any trigger, data source, or integration in Workflows. Use cases range from auto-scaling Kubernetes clusters based on Dynatrace Intelligence forecasts to generating query-cost-optimization recommendations for stakeholders, to virtually any other automation your environment requires.

Using agents through the Dynatrace MCP Server

The Dynatrace MCP Server makes the agents available outside the Dynatrace web UI, without requiring you to deploy or operate any additional infrastructure. You can connect Dynatrace to any MCP-compatible client in minutes, with no server to install, host, or maintain.

Through the tools exposed by the MCP Server, you can use natural language to query data in Grail®, check system health, and get problem analyses and remediation recommendations. This brings Dynatrace directly into the tools you already use, such as your IDE, Claude Code and Cowork, Microsoft Copilot, Slack, or automation platforms like n8n. The Dynatrace MCP Server also powers integrations with systems like Azure SRE, AWS DevOps, GitHub Copilot, Atlassian Rovo Ops, Amazon Q, and others.

Figure 3. Dynatrace MCP server in Visual Studio Code with GitHub Copilot

This means agents are no longer tied to a single interface. You can ask Dynatrace questions and get grounded, production-ready answers wherever you work, using the same agents and intelligence that power Assist and workflows.

Dynatrace Assist: a simple way to test ready-made agents

The quickest way to use a ready-made agent and see how it works before you start creating a workflow is with . Dynatrace Assist lets you ask questions about your environment using natural language, without switching tools or setting anything up.

A simple way to start is with a real problem you already have. For example, when a service becomes slow, open Assist and ask a question such as "Summarize the open problems and highlight those that need immediate attention." Assist interprets the question, evaluates the environment you're working in, and pulls together relevant data and context using Dynatrace Intelligence. Instead of manually navigating metrics, traces, logs, and dependencies, you get an explanation grounded in what is actually happening in your system.

Continuing your conversation with Assist, you can refine the question or follow suggested drill-downs. Assist supports this as a single flow, helping you move from an initial question to deeper analysis and, where applicable, to next steps. You're not configuring an agent or defining behavior. You're simply asking a question and letting Dynatrace coordinate the right intelligence and ready-made agents behind the scenes.

Figure 5. Dynatrace Assist

This makes Assist your lowest-friction entry point for using Dynatrace agents. You get a concrete result quickly, using the same data and context you already rely on in your daily work.

What's next?

If you haven't already, open Dynatrace Playground, or your Dynatrace tenant, and ask Dynatrace Assist a question to see the ready-made agents in action.