Dynatrace announced the next generation of its Artificial Intelligence, Davis, now powered by new and enhanced algorithms, and an ability to ingest data and events from third-party solutions.

“Four years ago, we pioneered, and continually improve, a unique, deterministic approach to AI that enabled customers to simplify enterprise cloud environments and focus more time on innovation. Because Dynatrace auto-discovers and maps dependencies across the enterprise cloud and analyzes all transactions, our Davis AI engine can truly causate, and drive to the precise root cause of issues versus simple guesses based on correlation. This concept just got even better through semantically enriching external data and mapping it to our real-time topological models. In addition, unlike other solutions, it doesn’t require learning periods, making it effective for highly dynamic clouds,” explains Bernd Greifeneder, CTO at Dynatrace.

“With today’s added capabilities, Davis’ power has increased significantly providing precise answers with even more relevance, plus additional context via third-party data and events. This enables greater automation and leads the way to autonomous cloud operations.”

Key AI capability enhancements include:

- Open platform that is smarter and broader – Dynatrace’s Davis is now able to ingest custom metrics, data and events from third-party solutions such as CI/CD and ITSM tools, enabling Dynatrace® to deliver more precise answers with deeper context. Integrations include F5, IBM DataPower, Citrix NetScaler, ServiceNow, Puppet, Chef, and more.

- Easier and more automatic – Davis has been enhanced with algorithms that are better able to detect performance variations without relying on thresholds or baselines. Full stack, high fidelity data analysis means better grouping of disparate alerts and single root cause determination with precise accuracy.

- Deterministic answers for automation and self-healing – By providing a broader range of precise and actionable problem identification, impact analysis and root cause, Davis is able to power auto-remediation workflows and self-healing.

Unlike competitive AIOps or traditional monitoring tools that rely on machine learning to surface correlation data, Davis thrives in dynamic cloud environments in which there is no time to “learn”. Davis uses real-time dependency knowledge with full-stack context to go well beyond correlation engines to deliver precise causation of problems automatically. As a result, underlying causes of alert noise are eliminated, and only deterministic answers are surfaced, paving the way for auto-remediation and ultimately, autonomous cloud operations.