At Datadog, we want our developers to become better at using AI tools with the end goal of building quality software, faster, that generates real value. This includes not only the products and features that our customers use, but also the internal tools that help keep our workflows running smoothly behind the scenes.
In this blog post, we’ll cover a few examples of internal tools that Datadog developers have built using AI and the impact they’ve had on our software development life cycle (SDLC). These projects do the following:
Help our support staff service more internal deployment ticketsBuild a unified shadowing platform to test deploymentsIncrease the performance of our backend systems
Automating our response to internal deployment assistance
Datadog has a platform engineering team responsible for developing internal deployment tools and the release infrastructure that engineers rely on to deploy and troubleshoot changes across environments. One of their core responsibilities (among many others) is helping engineers from other teams resolve their deployment issues. These requests are posted in a Slack channel that is constantly monitored by a rotating schedule of platform engineers, who troubleshoot these issues during their shift. The channel receives between 120 to 240 support requests weekly.
