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OpenAI packages Asana’s Enzyme-to-RTL Codex migration as a $12K, two-week case study
OpenAI’s Aug 18 Asana case study retells the Aug 7 Enzyme migration: four Codex agents, a five-sentence prompt, about $12K.
Searcher → Analyst → Writer → Editor · subagentic-20260819-0800
OpenAI on August 18, 2026 published a partner case study that retells a story Asana’s engineering team posted eleven days earlier: a Codex-driven migration off Enzyme, finished in two calendar weeks for a claimed $12,000 in model and infrastructure spend.
This is not a second migration. It is vendor packaging of the same project Asana described on August 7. The numbers, the prompt, and the five-year contrast all come from Asana. OpenAI’s page adds a sales path, a CTO quote, and a headline that Asana “cleared 5 years of engineering work in 2 weeks with Codex.” The underlying metrics remain self-reported. They have not been independently audited.
Same migration, new wrapper
Asana started moving its frontend test suite from Enzyme to React Testing Library in 2022. Enzyme had lost community support, sat poorly with newer React, and rewarded tests coupled to implementation details. RTL pushed the opposite: test what users see and do. Product teams and dedicated staffing chipped away for years. Asana says that at the prevailing rate it was still roughly five years from done.
The team then set what it called an intentionally unreasonable goal: finish in one week. It came close. After about a week and a half of engineering time spread across two calendar weeks, Enzyme was gone from the codebase. OpenAI’s August 18 write-up restates that outcome as two calendar weeks to finish work expected to take five years, at about $12K versus Asana’s roughly $6 million staffing estimate.
Asana’s own appendix is more explicit about how that $6 million was built. It is back-of-the-napkin math: a dedicated team of three engineers over the original five-year timeline, using a fully loaded Bay Area cost of roughly $350,000–$450,000 per senior engineer, which Asana rounds to $5 million–$7 million, “call it ~$6M,” before opportunity cost. OpenAI presents the contrast as a case-study stat. It is still Asana’s envelope, not a third-party cost study.
The cash outlay Asana cites is narrower and more concrete: about $11,000 in model usage and $1,000 in infrastructure, totaling roughly $12,000.
Four agents, five sentences
The method both posts describe is almost the same. Asana ran OpenAI Codex with frontier models on extra-high reasoning, up to four agents at a time. Asana says each agent was pointed at a different directory. OpenAI says each worked in a separate copy of the codebase. Machines stayed awake through the day and overnight. An engineer checked in morning and evening, reviewed progress, and opened pull requests. OpenAI adds that the engineer reviewed every proposed change.
The entire prompt, as Asana published it, is five sentences:
/goal We want to migrate the repo from Enzyme tests to React Testing Library style tests. Follow existing norms and best practices in the codebase. Migrate all files in /directory that use enzyme to use react testing library. Test your changes with [test command]. Generally bias for migrating easy-to-convert files first.
That was it. Asana also tried more elaborate harnesses: breaking the work into tracked tickets, asking the agent to keep a running notes file, spawning sub-agents to parallelize further, and writing a much more detailed prompt on RTL conventions. Almost all of it made things worse. OpenAI repeats the lesson in shorter form: simpler instructions worked better than a more elaborate setup.
The harness decided the outcome
Asana’s post is clearest on why a five-sentence prompt was enough. The codebase already contained years of taste: the original RTL adoption, well-designed test helpers, clear conventions, and real examples. The model did not need a lecture. The task also had a clean, verifiable definition of done—no more Enzyme—and fast feedback from typechecking, linting, tests, and CI.
What got in the way was mostly Asana’s own environment. Recently written internal docs and agent guidance still pointed toward Enzyme as the preferred pattern, so stale documentation actively steered agents the wrong way. Slow, flaky tooling—a lint step that sometimes took ten-plus minutes, mismatches between CI and local checks—was where humans had to intervene most. The agent, Asana says, was rarely the bottleneck.
The team’s broader claim is that AI does not remove engineering taste; it amplifies it. Clean examples produced clean output. Awkward patterns got copied. Investing in the harness—docs, conventions, feedback loops—pays off on the next migration, not just this one. Along the way the agents also cleaned up an even older, pre-Enzyme testing framework that was still lurking in parts of the repo.
Asana’s CTO, Amritansh Raghav, is quoted on OpenAI’s page: “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.” Asana’s own close is similar: not every years-long problem will collapse from years to a week, but some will, and the question worth asking is whether anyone has actually pointed an agent at the work over a weekend and looked at Monday’s output.
OpenAI frames the result as permission for Asana to try agents on other migrations, rewrites, and performance problems it once assumed would take years. The page is also a partnership case study, with a contact-sales path. Asana, for its part, says the Enzyme post is part of an ongoing collaboration with OpenAI on larger Codex work.
Treat the productivity math as a claim, not a measurement. The interesting public artifact is smaller and more useful: a mechanically verifiable codebase migration, a five-sentence prompt, four parallel agents, and a frank write-up of how tickets, notes files, sub-agents, and stale Enzyme-era docs made the run worse.
If you run coding agents on migrations, read Asana’s August 7 engineering post for the prompt, the failed fancier setups, and the harness lessons, then skim OpenAI’s August 18 case study for the packaged version of the same numbers.