AI is changing the reliability game for SREs
The integration of AI into production workloads is complicating SRE efforts by introducing new failure modes and shifting operational toil toward the supervision of unpredictable m…
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From AI experiments to business outcomes: How to measure what matters
Many enterprises struggle to demonstrate AI ROI because they focus on technical metrics, such as model accuracy, rather than the actual impact on end-to-end business processes. To …
From millions of log lines to actionable patterns in seconds with logs pattern analysis
Dynatrace's new log pattern analysis feature, currently in preview, automatically transforms massive volumes of unstructured log data into a handful of identifiable, actionable pat…
Dynatrace and Arize bring full-lifecycle observability to AI applications
Dynatrace has entered a definitive agreement to acquire Arize, aiming to merge AI-native evaluation with enterprise-scale observability. This integration will bridge the visibility…
Why AI agents need an AI lakehouse in the modern enterprise
As enterprises move toward an "agentic" model, traditional data architectures are being replaced by AI lakehouses that provide the real-time, semantic context necessary for autonom…
Coding agents are scaling fast. How do teams keep up?
The rapid adoption of AI-assisted development has created a "visibility gap," where AI-generated code can pass initial tests but introduce complex, undocumented errors that only ma…
Query Dynatrace Managed data directly in Claude, AWS Kiro, GitHub Copilot, and other MCP-compatible clients
The Dynatrace Managed MCP Server is a protocol adapter that allows AI assistants, such as Claude Desktop and GitHub Copilot, to access and query self-hosted Dynatrace Managed data …
Our GitOps Journey in the AI Era
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Quit trying to keep up with every new AI tool and keep building
To navigate the overwhelming hype surrounding new AI coding tools, developers should focus on identifying creators who demonstrate real value through public work and shared impleme…
Smarter, safer Agentic AI: Dynatrace observability meets NVIDIA AI-Q
Dynatrace provides full-stack observability for complex agentic AI systems by monitoring everything from GPU infrastructure and model performance to token consumption. Through its …
AI Infrastructure Monitoring: Why Your AI Stack Is Only as Strong as What Runs Underneath
Traditional monitoring methods are inadequate for the unpredictable, bursty nature of AI workloads, which can cause sudden and extreme strain on GPUs, networks, and storage. To ens…
Beyond LLM-as-a-judge: Establishing LLM evaluations as a foundation for trustworthy agentic AI systems
This article explores how LLM evaluations provide a systematic framework for measuring the quality, accuracy, and safety of probabilistic AI models as they move from experimentatio…