Operations | Monitoring | ITSM | DevOps | Cloud

AI coding assistants are quickly becoming a core part of software engineering workflows, helping developers write, refactor, and review code faster. But without effective monitoring, it can be difficult to know whether these tools are performing reliably and proving useful to engineers. As organizations scale their use of tools like Claude Code, key questions emerge.

Engineering teams rely on forms for everything from approvals to checklists, yet the process usually lives outside engineering operations. Spreadsheets, one-off surveys, and external form builders capture inputs, but they create scattered data, slow follow-ups, and manual translation into actionable work. Datadog Forms enables teams to create and share interactive forms directly within Datadog.

Teams deploying LLM applications face a critical blind spot: They can measure speed and cost, but not whether their AI is actually giving good answers. To build user trust in these applications, teams also need to measure response quality, including factual accuracy, safety, and tone. Operational metrics show how a system behaves, but not whether its responses are correct or on brand.

Datadog App Builder is a low-code tool for creating internal apps, making use of a drag-and-drop interface that allows engineering teams to troubleshoot issues, optimize operations, and enable self-service while connecting directly to their Datadog data and permissions. Now, with conversational AI, teams can go from idea to working prototype even faster.

As organizations grow, engineering leaders often need to drive cross-team initiatives such as reducing cloud spend, upgrading runtimes, or strengthening security controls. Tracking this work can quickly become fragmented across spreadsheets, dashboards, and status meetings. Progress is hard to measure, accountability is unclear, and the impact of each effort can be difficult to demonstrate.

Today, many DevOps and security teams operate in a world of complex, hybrid, or multi-vendor environments. As more teams look to avoid lock-in by adopting open standards, OpenTelemetry (OTel) is quickly gaining adoption as the primary open source method for DevOps and security teams to instrument and aggregate their telemetry data. However, OTel alone may lack the advanced processing functions, native volume control rules, and hybrid environment support that large organizations need.

As major incidents like AWS’s October 2025 outage illustrate, modern systems are immensely interconnected. A failure in one can lead to a cascade of downstream problems. In this case, issues with DNS resolution for DynamoDB led to widespread disruptions with other AWS services and, subsequently, thousands of applications and services that rely on that infrastructure.

Over the past three decades, Ruby has assumed a pivotal role in the modern web stack and become a fixture in the tool kits of countless DevOps and platform teams. Today, it is a driving force in contemporary application development, testing, automation, and CI/CD. For this blog post, we used data from our always-on continuous profiling of more than 3,000 real-world services from hundreds of organizations to track trends in Ruby usage and performance.

Building automated workflows that adapt to real-world complexity can be a challenge. As systems scale and scenarios multiply, teams often end up hardcoding endless logic branches just to handle every potential outcome. That’s why we’re introducing Datadog Agent Builder, a powerful new tool that lets you create custom AI agents that are fully hosted by Datadog.

Backstage is a popular open source framework for building internal developer portals (IDPs) used by organizations to aggregate service metadata and create a single source of truth for their software developers. However, data stored in the Backstage Software Catalog can quickly become siloed and inaccessible from monitoring tools such as Datadog.