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datadog

Instrument LangGraph agents with Datadog: a practical guide

May 28, 2026 By Datadog In Datadog
AI agents tend to function as black boxes, and it can be difficult to trace and understand agent workflows end-to-end in order to characterize performance. Particularly, you need visibility into the following: By tracing full agent runs with LLM Observability, Datadog AI Agent Monitoring enables you to visualize workflows with flame graphs and quickly spot sources of failures and latency.
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Monitor JavaScript framework routing with Datadog RUM

May 28, 2026 By Datadog In Datadog
Modern web applications rely on frameworks like Next.js, Vue, and Angular to handle routing and rendering. In these architectures, navigation happens within the application rather than through full page loads, which makes it difficult for traditional browser instrumentation to capture what users actually experience. As a result, teams often see misleading view names, missing navigations, and errors that are either misattributed or not captured at all, especially during hydration or lazy loading.
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Monitor Azure Managed Redis with Datadog

May 28, 2026 By Michael Cronk In Datadog
Azure Managed Redis is Microsoft’s fully managed, enterprise-tier in-memory data store. It is designed for the low-latency caching, session storage, and real-time data needs of modern applications, including AI workloads that depend on fast vector and embedding lookups. Because user-facing applications often query Redis directly, even small regressions in latency, hit rate, or memory pressure can degrade the user experience.
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Deploy Datadog Kubernetes Autoscaling at scale

May 28, 2026 By Danny Driscoll In Datadog
Every Kubernetes environment accumulates waste over time. Teams overprovision CPU and memory requests to avoid performance risk, run idle replicas to preserve headroom, and leave Horizontal Pod Autoscalers (HPAs) untouched long after workload behavior has changed. Some of this waste can be addressed at the node level, where Datadog Cluster Autoscaling helps teams rightsize capacity.
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Unified observability for Alibaba Cloud with Datadog

May 28, 2026 By Ellie Cohen In Datadog
Alibaba Cloud is a major cloud provider in APAC, offering industry-leading foundational AI models in addition to compute, managed databases, object storage, and Kubernetes through its Container Service for Kubernetes (ACK). Teams choose Alibaba Cloud for its infrastructure availability across Asia Pacific and its managed services. For SREs and platform engineers, that often means running Alibaba Cloud alongside AWS, Google Cloud, or Microsoft Azure.
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Investigate funnel drop-offs with Product Analytics

May 27, 2026 By Datadog In Datadog
For most product teams, funnels are a staple of the analytics toolkit despite a frustrating limitation. You can see which step users are dropping off at, but understanding why requires hours of manual slicing across segments, separate comparison views, and a lot of trial and error before you land on a useful hypothesis. And even when you find something meaningful, taking action typically means jumping to another tool, building a new segment, or filing a request with a data team.
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Ameet Talwalkar on Building the AI Research Lab

May 26, 2026 By Datadog In Datadog
"We're doing cutting-edge AI, focused on real translational impact: getting our research over the wall and into production." Ameet Talwalkar, Datadog's Chief Scientist, shares what it took to build the AI Research Lab from the ground up — and what makes DAIR different from traditional research teams. At Datadog, research ships. Recent work from the lab includes Toto 2.0, open-weights time series forecasting models ranked on leading benchmarks, and ARFBench, a new benchmark for evaluating AI on real incident data.
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Measure the real impact of AI coding tools on software delivery with Datadog AI Impact

May 26, 2026 By Datadog In Datadog
Engineering teams have rapidly adopted AI coding tools, but organizations still struggle to understand their impact. Existing dashboards focus on activity, such as daily active users, acceptance rates, or lines of generated code, but these metrics don’t answer a more important question: Are teams actually shipping more, faster, and with fewer issues?
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How to measure developer experience (DevEx) in the AI era

May 22, 2026 By Datadog In Datadog
As AI coding assistants dramatically inflate PR counts, commit frequency, and lines of code, the limitations of individual output metrics have never been more apparent. A developer can now produce significantly more lines per session, but higher volume doesn’t guarantee that the code is stable, maintainable, or successfully running in production. GitClear analyzed over 200 million lines of code and found that code churn nearly doubled following widespread AI adoption.
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