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Dynatrace Delivers Software Intelligence as Code

Dynatrace announced it is delivering software intelligence, including broad and deep observability, application security, and advanced AIOps capabilities, as code.

This enables developers who are adopting everything-as-code practices to easily incorporate software intelligence capabilities into their applications. As a result, they can automate the orchestration of all resources across the software development lifecycle that are required to deliver cloud-native applications and infrastructure at scale. In addition, developers can ensure their applications achieve standards and service level objectives (SLOs) for critical metrics, including performance, quality, and security, or automatically initiate corrective action when these standards are not met. These enhancements to Dynatrace® help development teams bring higher quality, more secure innovations to market faster, and with greater efficiency.

By enabling developers to access libraries of templates for reusable configurations, Dynatrace makes it easier for development teams to establish and adhere to organizational best practices for observability and security, without adding friction to the development process. This is made possible through additional application program interface (API) endpoints, which enable and extend configuration-as-code for multiple Dynatrace capabilities, including anomaly detection and alerting, dashboarding and analytics, and data enrichment.

“Organizations adopting practices like GitOps and infrastructure-as-code also require observability and automation-as-code to increase speed and resiliency,” said Steve Tack, SVP of Product Management at Dynatrace. “Unlike alternative solutions that stop with basic metrics and require manual configuration, Dynatrace extends to intelligent observability, advanced AIOps, and application security. These capabilities drive real-time actions to ensure teams achieve SLOs and optimize critical business metrics. This enables development, DevOps, and SRE teams to bring high quality, secure innovations to market faster, and at enterprise-scale.”

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Dynatrace Delivers Software Intelligence as Code

Dynatrace announced it is delivering software intelligence, including broad and deep observability, application security, and advanced AIOps capabilities, as code.

This enables developers who are adopting everything-as-code practices to easily incorporate software intelligence capabilities into their applications. As a result, they can automate the orchestration of all resources across the software development lifecycle that are required to deliver cloud-native applications and infrastructure at scale. In addition, developers can ensure their applications achieve standards and service level objectives (SLOs) for critical metrics, including performance, quality, and security, or automatically initiate corrective action when these standards are not met. These enhancements to Dynatrace® help development teams bring higher quality, more secure innovations to market faster, and with greater efficiency.

By enabling developers to access libraries of templates for reusable configurations, Dynatrace makes it easier for development teams to establish and adhere to organizational best practices for observability and security, without adding friction to the development process. This is made possible through additional application program interface (API) endpoints, which enable and extend configuration-as-code for multiple Dynatrace capabilities, including anomaly detection and alerting, dashboarding and analytics, and data enrichment.

“Organizations adopting practices like GitOps and infrastructure-as-code also require observability and automation-as-code to increase speed and resiliency,” said Steve Tack, SVP of Product Management at Dynatrace. “Unlike alternative solutions that stop with basic metrics and require manual configuration, Dynatrace extends to intelligent observability, advanced AIOps, and application security. These capabilities drive real-time actions to ensure teams achieve SLOs and optimize critical business metrics. This enables development, DevOps, and SRE teams to bring high quality, secure innovations to market faster, and at enterprise-scale.”

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In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...