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AI Agents Create Their Own Monitoring Problem. Datadog and Dynatrace Are Racing to Own It

AI coding agents are supposed to make software cheaper to build. They may also create more software changes, production failures and machine-generated complexity to inspect. That second-order effect connects Datadog, Inc. (NASDAQ:DDOG)…

Yahoo Finance

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Sep 12, 2026 at 10:56 PM UTC · Updated vor 2 Tagen · 2 Min. Lesezeit

AI Agents Create Their Own Monitoring Problem. Datadog and Dynatrace Are Racing to Own It
Image via Yahoo Finance

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$1.12B Datadog Q2 revenue

Last Updated

vor 2 Tagen

Übersetzung…

AI coding agents are supposed to make software cheaper to build. They may also create more software changes, production failures and machine-generated complexity to inspect. That second-order effect connects Datadog, Inc. (NASDAQ:DDOG) and Dynatrace, Inc. (NYSE:DT) to an observability market that AI could expand rather than erase.

Datadog told the Goldman Sachs Communacopia + Technology Conference on September 10 that monetization of AI-agent observability has begun, with thousands of customers using the capability. Management described an emerging "inference economy": more AI applications and agents mean more activity that customers need to monitor. That shifts the opportunity from watching human-written releases toward tracing continuous decisions made by software that can change software itself.

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One day later, Dynatrace launched a verified Cursor Marketplace plugin. A single installation gives coding agents access to live production context through MCP and 30 Dynatrace skills. The timing makes the competition tangible: Datadog is framing the category, while Dynatrace is putting production telemetry directly inside an agent's workflow.

Usage can grow faster than seats

Datadog has the stronger recent growth profile. Second-quarter revenue rose 36% to $1.12 billion, and customers generating at least $100,000 of annual recurring revenue increased 23% to roughly 4,720. Its usage-based model can capture expanding telemetry and inference volume. The risk is the same sensitivity to optimization that has periodically hurt cloud vendors; more capable agents could also resolve some incidents.

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