Dynatrace

Dynatrace announced domain-specific AI agents in January 2026 that autonomously handle recurring operational tasks for site reliability engineers (SREs), developers, and security teams — such as investigating incidents, triaging vulnerabilities, and coordinating remediation across enterprise tools like ServiceNow, GitHub, and Jira.

Announced at Perform 2026 (January 28, 2026), Dynatrace Intelligence Agents are built on Dynatrace Intelligence, described as an agentic operations system combining deterministic causal AI with generative AI. The agents are organized into three tiers: foundational agents providing causal reasoning and real-time context; domain agents for SRE/DevOps issue prevention, business observability, and security operations; and assist agents that interpret situations in natural language. When activated by a detected anomaly or user request, the system automatically mobilizes the relevant agents to assess context, determine urgency, and execute actions — such as auto-enriching incident tickets and triggering remediation runbooks — through existing enterprise tools. Human oversight and approval remain part of the workflow by design.

Dynatrace deployed Davis AI as part of its Application Security product to continuously scan running applications for software vulnerabilities in real time, automatically prioritize the most critical ones based on actual runtime exposure, and recommend remediation steps — reducing the volume of security alerts that security teams need to manually review.

Dynatrace Runtime Vulnerability Analytics monitors loaded libraries and runtime components in production environments, matching them against vulnerability feeds and the National Vulnerability Database (NVD) automatically. Unlike traditional scanners that report all known vulnerabilities, Davis AI applies runtime context — such as whether a vulnerable library is actually being called, whether a service is exposed to the public internet, and whether sensitive data is within reach — to produce a Davis Security Score (DSS) that re-ranks risk for each specific environment. The platform can automatically block certain attacks in real time and trigger workflow automation to create vulnerability remediation tickets in tools like Jira or ServiceNow.

Dynatrace offers Davis AI predictive forecasting that analyzes historical resource usage data to predict when cloud infrastructure — such as disk space, CPU, or memory — is likely to run out, allowing teams to act before an outage occurs rather than reacting after the fact.

Davis AI uses an AutoML (automated machine learning) approach that analyzes time series data stored in the Grail data lakehouse, detecting variance, seasonality, and trends to select the best forecasting model automatically. The system can predict resource consumption for thousands of individual components in parallel — for example, Dynatrace's own internal infrastructure tracks over 8,000 disks using this approach. For Kubernetes workloads, predictive AI can be combined with generative AI to automatically open pull requests on GitHub, suggesting scaling adjustments to manifest files for engineer review.