Operations | Monitoring | ITSM | DevOps | Cloud

As large language models (LLMs) grow more powerful, organizations are deploying agentic AI applications to tackle complex, multi-step tasks. With Amazon Bedrock Agents, developers can orchestrate these agents to manage tasks such as triggering serverless functions, calling APIs, accessing knowledge bases, and maintaining contextual conversations—all while breaking down complex user requests or tasks into manageable steps.

DASH 2025 brought the Datadog community together like never before. During our biggest event yet, thousands of attendees gathered at the North Javits Center in New York City for two and a half days of content, learning, and community, where they deepened their knowledge and connected with peers. Here's a quick look back at some of the highlights from this year's DASH.

As your application grows in complexity, identifying the root cause of issues becomes increasingly difficult. Many monitoring strategies make this even harder by siloing frontend and backend data. To effectively troubleshoot problems that spread across your app, you need visibility not just into each part of your stack, but also into how these parts interact.

We are thrilled to announce that, for the fifth consecutive year, Datadog has been named a Leader in the 2025 Gartner Magic Quadrant for Observability Platforms. We believe that this recognition reflects our continued focus on helping customers observe, secure, and act on everything that matters across their technology stack.

When an error occurs, developers need to act quickly. But too often, they’re left searching through stack traces without enough context to understand what happened, who owns the code, or what change may have introduced the issue. This slows down triage, creates inefficient handoffs, and takes time away from building new features.

As organizations rapidly scale their use of large language models (LLMs), many teams are adopting LiteLLM to simplify access to a diverse set of LLM providers and models. LiteLLM provides a unified interface through both an SDK and proxy to speed up development, centralize control, and optimize LLM-powered workflows. But introducing a proxy layer adds abstraction, making it harder to understand how requests are processed.

Data lineage is the evolutionary history of datasets. More concretely, lineage is metadata that captures the flow and transformation of data in data pipelines, also called the data lifecycle.

For on-call engineers responding to alerts, every minute counts. Faster incident response means faster mitigation, reduced downtime, and better customer experience. But even the most finely tuned, meticulously detailed alerts can leave responders scrambling for more information. In order to effectively triage and investigate incidents and set remediation in motion, responders need data to help them contextualize alerts.

For many organizations, scaling up their systems means incorporating new tools to build out infrastructure, optimize code performance and security, improve communication, and track cost changes. While these changes are necessary to support an increased workload, they often result in a situation where even the most basic tasks involve switching between multiple platforms.

Building on our FedRAMP Moderate authorization and our “In Process” status for FedRAMP High, Datadog for Government is now "In Process" for GovRAMP High Authorization, giving agencies a unified observability platform that meets the toughest public-sector security bars.