Rigid scripts left complex migrations manual. Now, a background agent turns natural language prompts into verified, merged pull requests.
The world's most popular audio streaming service serves over 713 million users while managing a massive codebase across thousands of repositories.
Automated code updates relied on rigid scripts and abstract syntax tree manipulation that required specialized expertise, limiting automation to...
“Our engineers are now able to execute fleet-wide migrations at a pace that simply wasn't possible before. It's a promising sign of how AI can reduce the complexity of maintaining large code bases and a clear signal of how AI is reshaping development velocity.”
Music and podcast streaming platform with personalized audio discovery.
Anthropic is a technology company specializing in artificial intelligence and machine learning solutions.
Spotify's Automated code migration is part of this use case:
Related implementations across industries and use cases
Code grew 7x faster than headcount; hundreds of teams ran identical migrations for months. Agents now author and auto-merge the bulk of PRs.
A two-year Ruby rewrite became an eight-week sprint—agents mapped the codebase before engineers wrote a line of new code.
Designers relied on static mocks and engineering queues. Now they build live apps directly, shrinking 6-month cycles to 8 weeks.
Code grew 7x faster than headcount; hundreds of teams ran identical migrations for months. Agents now author and auto-merge the bulk of PRs.
A two-year Ruby rewrite became an eight-week sprint—agents mapped the codebase before engineers wrote a line of new code.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Complex headcount reporting meant tedious manual data slicing. Now, HR uses AI prompts to instantly generate CFO-ready talent insights.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Colleagues hunted through knowledge systems for answers; Global Markets hand-sorted emails before ops could act. Claude now handles both.
Rigid scripts left complex migrations manual. Now, a background agent turns natural language prompts into verified, merged pull requests.
The world's most popular audio streaming service serves over 713 million users while managing a massive codebase across thousands of repositories.
Automated code updates relied on rigid scripts and abstract syntax tree manipulation that required specialized expertise, limiting automation to...
“Our engineers are now able to execute fleet-wide migrations at a pace that simply wasn't possible before. It's a promising sign of how AI can reduce the complexity of maintaining large code bases and a clear signal of how AI is reshaping development velocity.”
Music and podcast streaming platform with personalized audio discovery.
Anthropic is a technology company specializing in artificial intelligence and machine learning solutions.
Spotify's Automated code migration is part of this use case:
Related implementations across industries and use cases
Code grew 7x faster than headcount; hundreds of teams ran identical migrations for months. Agents now author and auto-merge the bulk of PRs.
A two-year Ruby rewrite became an eight-week sprint—agents mapped the codebase before engineers wrote a line of new code.
Designers relied on static mocks and engineering queues. Now they build live apps directly, shrinking 6-month cycles to 8 weeks.
Code grew 7x faster than headcount; hundreds of teams ran identical migrations for months. Agents now author and auto-merge the bulk of PRs.
A two-year Ruby rewrite became an eight-week sprint—agents mapped the codebase before engineers wrote a line of new code.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Complex headcount reporting meant tedious manual data slicing. Now, HR uses AI prompts to instantly generate CFO-ready talent insights.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Colleagues hunted through knowledge systems for answers; Global Markets hand-sorted emails before ops could act. Claude now handles both.