Agentic AI provided by Dynatrace and Atlassian transforms complete incident management

Engineers and incident managers frequently lack the production visibility they need to quickly identify and address underlying issues when incidents occur. The majority of tickets lack information about the severity, impact, or next steps. This forces teams to waste time jumping between tools and manually stitching data together, delaying recovery and driving up costs.

By directly integrating real-time production insights into incident management procedures, the brand-new Dynatrace integration with Atlassian addresses this issue. Without having to switch tools, teams can instantly see what’s going on, who is affected, and what needs to be done to solve problems faster. By comprehending the topology, data context, and dependencies of your entire digital ecosystem, Dynatrace is able to identify issues in real time in a unique way. Teams are provided with a complete, production-accurate, “live” picture of the problem’s details, severity, and impact when incidents are automatically linked to its underlying root causes. Atlassian Rovo’s human-readable summaries now provide access to Dynatrace insights in Jira Service Management. You can significantly shorten the mean time to resolution (MTTR) and speed up response times by directly integrating production context into Jira, Confluence, and Jira Service Management. Context is our mantra at Dynatrace, and it drives everything we do. More than just data enrichment, this means that dependencies are mapped and every piece of data is automatically contextualized. However, context also entails providing the appropriate data at precisely the right time and place. Dynatrace is bringing these insights directly into Atlassian in order to accomplish exactly that. This goes beyond just IT service management (ITSM). You can get access to contextualized insights directly within an IDE as described in our latest blog post about the new Dynatrace MCP Server.
With production context at your fingertips, you can diagnose more quickly. An engineer receives the majority of incident tickets with nothing more than a timestamp, a vague description, or a user complaint. They rarely disclose the extent of the problem, the affected systems, or any potential causes. Teams are forced to waste time searching through monitoring dashboards, chasing logs, and switching between tools in order to comprehend the fundamentals of the issue because of the absence of context in an ITSM workflow. You can now immediately ask the Rovo Ops agent to identify anomalies that occurred during the incident period, avoiding frustration. The agent queries Dynatrace via our MCP Server and returns the findings directly in the same browser window.

You immediately gain clarity regarding health, the problem, the impact, and the evidence when you have contextual details and alerts available directly in the ticket context. This speeds up diagnosis and recovery, reduces unnecessary escalation of previously known or related issues, and prevents internal resources from being tied down by redundant work. Utilize AI-driven automation and root cause analysis to remediate more effectively. The Rovo Ops agent uses Dynatrace production insights to accelerate incident managers’ triage and root-cause analysis, locating the actual cause in real time and providing a higher level of insight and accuracy once an incident is identified. Rovo can now pull in Dynatrace Causal AI insights, including the precise root cause and blast radius of the issue, and combines these with Jira Service Management incident and change history. Rovo outperforms the guesswork of pure GenAI approaches by providing fact-based, AI-generated problem summaries and clear remediation recommendations with Dynatrace contextual intelligence. From this point, just follow the remediation recommendation and trigger a suggested automation action in Jira Service Management, or ask follow-up questions for clarification.

Automated post-incident reviews help you prepare for the future. After an incident has been mitigated and marked as resolved in Jira Service Management, the work is not finished because you still need to record what happened and figure out how to prevent it from happening again. Instead of spending hours on manual write-ups, Rovo automatically triggers the post-incident review (PIR) process.

Rovo presents all of the pertinent information and history in the auto-generated PIR, from the root cause to the anomalies that were found. These details and history are all enhanced by insights driven by Dynatrace AI. This combines Jira Service Management context attributes like assignees, tags, outage duration, and related change logs to present a comprehensive, time-ordered view of the incident. A draft PIR is created by the agent in this context. A pre-filled prevention plan, a clear summary of the cause, and monitoring charts that show the status before, during, and after the incident are all contained within the PIR. The PIR can now be reviewed, improved upon, and completed by you. The automatically documented PIRs act as built-in retrospectives, helping teams continuously mature their operations. Additionally, they provide insights that Rovo uses to refine its future recommendations. Change your work environment beyond incident management. The extended Dynatrace + Atlassian integration now allows for a variety of new possibilities, such as the ones listed here. We’ll continue to explore deeper integrations to make your troubleshooting journey even more efficient in the future.

Imagine directly following up on investigations from within Rovo, with seamless drill-downs into Dynatrace® Apps, or surfacing related post-mortem information and runbooks stored in Jira or Confluence to SREs when investigating an issue in Dynatrace.

Furthermore, the potential consequences extend far beyond incident management. More teams and roles can utilize the full potential of agentic AI by incorporating reliable, real-time production truth into daily workflow and directly connecting that truth to business outcomes. Get immediate release validation: Developers can query Rovo for pre- and post-deployment failure rates, SLOs, and outcome metrics, enabling them to roll back faster when necessary, release with confidence, and validate hypotheses with actual data. Make decisions based on outcomes: Product managers can ask Rovo or Davis CoPilot® to analyze the impact of a new feature or release by investigating KPI shifts such as user engagement or a drop in check-outs.

Support engineers working on Jira tickets can see Dynatrace insights regarding the root cause, blast radius, affected applications, and services to speed up triage based on business impact. Engineers can immediately conduct an impact analysis before assigning tickets thanks to the addition of additional information on the impact on users and businesses to these insights. Run smarter daily stand-ups: Development teams receive ready-made summaries, including exceptions, user analysis, and deployment reports from the last 24 hours, providing relevant insights into what’s actually happening in production.
With Dynatrace, your dependable foundation for agentic AI, you can begin reaping the benefits of deeper integrations. Dynatrace delivers a deep, causation-based understanding of your live digital systems, providing the precise, reliable insights that enterprises can trust as a foundation for agentic AI.
Are you ready to see how adopting agentic AI concepts will benefit Dynatrace and Atlassian? After that, use our remote MCP Server to learn more about the new possibilities or sign up for the preview to see for yourself how real-time production context will make your operations run more smoothly.