Instead of adopting an off-the-shelf coding agent as it is, we decided to build a small internal CLI. This CLI can delegate executing a prompt to an agent, run custom formatting and linting tasks using local Model Context Protocol (MCP), evaluate a diff using LLMs as a judge, upload logs to Google Cloud Platform (GCP), and capture traces in MLflow. Crucially, having that CLI allows us to seamlessly switch between different agents and LLMs. In the fast-moving environment that is GenAI, being flexible and pluggable this way has already allowed us to swap out pieces multiple times, giving our users a preconfigured and well-integrated tool out of the box, without exposing them to the nitty-gritty details.
See how Spotify evolved from golden paths, to deterministic code transformations, to background coding agents capable of building new features in one shot — or executing complex fleet-wide migrations.
Understand how our Backstage Software Catalog — which tracks ownership, dependencies, and other component metadata across thousands of repositories — gives AI agents the structured context they need to navigate your ecosystem on their own.
This approach has excelled in several areas, including:
Bumping dependencies in build files, such as in a Maven Project Object Model (POM) files
Updating configuration files, such as deployment manifests
Executing simple code refactors, like removing or replacing a deprecated method call
The impact has been significant, with a steady stream of automated pull requests being merged daily, keeping our codebases consistent, up to date, and secure. Since mid-2024, around half of Spotify’s pull requests have been automated by this system
Fleet Management is a powerful framework for applying code transformations across all of Spotify’s repositories. The idea is simple: We write small snippets of code that themselves modify source code and apply these transformations to thousands of software components. This way we can take care of maintenance tasks across our codebase.
On a high level, Fleet Management has been a roaring success. It has allowed us to automate away countless hours of toil, vastly reducing the time to roll out fleet-wide changes. The system works by running these source-to-source transformations as jobs, in a containerized environment, which then automatically open pull requests against the target repositories.
