Japanese tech giant Rakuten, whose services span e-commerce, finance, streaming, and other areas, has shared the results of implementing Codex, OpenAI's AI agent for writing and reviewing code. The reported figures are quite specific and worth noting.
What Codex Is and Why Development Teams Need It
In short, Codex isn't just a code autocompletion tool. It's an agent capable of independently handling development tasks: finding the causes of errors, proposing fixes, reviewing changes before release, and even helping to build complete features. A developer assigns a task, and the agent works on it, often without needing constant monitoring of every step.
To put it simply, it's more like a junior colleague to whom you can delegate routine tasks, rather than a smart suggestion in a code editor.
A 50% Reduction in Downtime – What's Behind It
One of the key metrics in development is MTTR (Mean Time To Resolution), the average time it takes to recover from an incident. It measures the time from when a failure occurs to when the problem is resolved. For large platforms like Rakuten, this is critical: every extra minute of downtime means that real users can't place an order, make a payment, or use the service.
According to the company, this metric has been reduced by 50% since implementing Codex. In other words, incidents that used to take an hour to resolve, for example, are now closed in about half an hour. This is achieved because the agent helps localize the problem and propose a working solution more quickly, saving engineers from having to sift through logs and test hypotheses from scratch.
Automating Routine but Important Processes
Another area where Codex has proven its worth is in checks within CI/CD processes. Without going into technical details, CI/CD is a pipeline where code undergoes a series of automated checks before reaching users. Some of these checks previously required human intervention: reviewing changes, ensuring nothing is broken, and authorizing the release.
Codex has taken over some of this work. The agent analyzes changes, identifies potential issues, and helps make decisions faster – without needing to involve a human reviewer for every routine check. This frees up engineers' time for tasks where human judgment is genuinely essential.
Complete Features in Weeks, Not Months
Perhaps the most fascinating aspect of Rakuten's case study isn't the acceleration of individual operations, but the fact that teams are now delivering complete product features from idea to production much faster. According to the company, full-stack solutions (meaning those that include both server-side and client-side components) are now being built in weeks, whereas this process previously took considerably longer.
This is a significant shift. Development speed isn't just a matter of team convenience; it's an indicator of how quickly a business can respond to market changes, test hypotheses, and deliver value to users. When the development cycle shrinks from months to weeks, the very logic of decision-making begins to change.
Safer, Not Just Faster
The emphasis on security is also worth mentioning. Rakuten's case study notes that Codex helps not only to accelerate development but also to make it safer. This isn't just a passing comment: automating checks and enabling a faster response to incidents directly impact the reliability of the services.
For a company on the scale of Rakuten, which handles millions of transactions and user data, security and stability are just as important as speed of delivery. The fact that an AI agent helps maintain a balance between these demands is arguably more significant than simply “writing code faster.”
What This Means for the Industry
Rakuten's case study is not the first, and certainly not the last, example of a major tech company integrating AI agents directly into its development lifecycle. But it is particularly noteworthy for its concrete details: it provides measurable results, not just vague platitudes about “transformation.”
It's also interesting that the conversation isn't about replacing developers. Codex fits into existing workflows as an additional team member – one that takes on routine tasks and helps people focus on more complex and creative challenges. At least, that is the picture Rakuten itself is painting.
The open question is how reproducible these results are for other companies. Rakuten is a large, mature tech organization with well-established processes and the resources for such an implementation. For smaller teams, the path to similar results may be different. But the very fact that an agent-based approach to development is starting to yield measurable results on this scale is a signal that's difficult to ignore.
