Using OpenAI Codex, Asana replaced an outdated testing system in two weeks for about $12K.

Products: Codex, API

Calendar weeks to finish work expected to take 5 years

$12K

Model and infrastructure cost versus Asana’s ~$6M staffing estimate

In about two weeks, Asana completed work it expected to take five years.

Asana helps teams coordinate work. Its platform uses AI agents and automations to help customers manage, track, and execute that work. Asana brings the same approach to its engineering organization, where people use OpenAI Codex, powered by frontier models, to tackle large codebase changes, then review and approve each proposed change.

For this project, Codex helped Asana’s engineers remove Enzyme, an outdated testing system that had made the company’s code harder to upgrade. Model and infrastructure costs came to about $12,000, compared with Asana’s roughly $6 million estimate for the previous staffing plan.

“Not every years-long project will collapse into weeks. But agents can give engineers more room for craft—and make once-impossible work worth attempting.”

—Amritansh Raghav, Chief Technology Officer, Asana

Their old testing tool, Enzyme, had fallen out of active maintenance and was becoming a blocker to modernizing Asana’s frontend stack. Asana used Codex, powered by frontier models, to do the work. From a five-sentence prompt, up to four coding agents worked in parallel, each in a separate copy of the codebase. An engineer checked progress twice a day and reviewed every proposed change. Simpler instructions worked better than a more elaborate setup.

After 1.5 weeks of engineering effort spread across two calendar weeks, Enzyme was fully removed. Model and infrastructure costs totaled about $12K. For comparison: the previous plan was expected to take at least five years and estimated to cost roughly $6M. The experience changed which long-running software projects the company believes are practical to take on.

With this migration complete, Asana can test agents on other migrations, rewrites, and performance problems it once assumed would take years. The team hopes this will give engineers more room to focus on craft while people continue to review the work.