Rakuten Cuts Incident Resolution Time in Half With AI

Japanese tech giant Rakuten has announced significant results from bringing AI into its software development process. The company integrated Codex, OpenAI's agent for writing and analyzing code, into its engineering pipeline, and the first measurable results look more than convincing. Mean time to resolve incidents fell by exactly half, and complex full-stack products that once took months to build now reach production in a matter of weeks.

Rakuten runs one of Asia's largest technology ecosystems, spanning e-commerce, financial services, streaming, telecommunications, and dozens of other businesses. Infrastructure that sprawling means a massive codebase, constant updates, integrations between services, and a high cost for any mistake. When a failure in a payment system or in a recommendation engine's logic directly affects revenue across millions of transactions, the speed of detecting and fixing incidents stops being just an operational KPI and becomes a strategic priority. That is why the choice of Codex doesn't look accidental: the company wanted a tool that could do more than generate code — one that could embed deeply into real engineering workflows.

OpenAI's Codex is more than a chatbot that writes functions on request. It is an agentic system that can carry out multi-step tasks: analyze repositories, find vulnerable sections of code, propose and even automatically apply fixes, and review changes inside CI/CD pipelines. That depth of integration is what sets Rakuten's approach apart from surface-level use of generative tools. Instead of just offering suggestions to developers, Codex becomes a full participant in the engineering process: it checks pull requests, flags potential regressions, and frees up teams' time for work that requires human judgment and architectural thinking.

A 50% reduction in MTTR may look like marketing exaggeration at first glance, but at Rakuten's scale it carries a very concrete economic meaning. Every minute of an incident in a high-load e-commerce system potentially means thousands of incomplete transactions, lost conversions, and damage to user trust. If an average team used to spend, say, two hours on diagnosis, isolation, and patching, the same cycle now fits into one hour. Multiply that by how often incidents occur in an ecosystem of this size, and the scale of the savings becomes obvious. Automated CI/CD review adds another layer: fewer bottlenecks when rolling out changes, fewer human errors in routine checks, and more room for iterative development.

The implications of this case reach well beyond one company. Rakuten is effectively showing that AI agents in software development are no longer experimental territory but a mature tool with measurable returns. For large technology organizations worldwide, this means rethinking the very model of engineering work: the developer's role is shifting from writing code to overseeing it, making architectural decisions, and managing automated agents. For startups and mid-sized companies, Rakuten's case opens a path to compete with giants by compressing time to market. For the industry as a whole, it is a signal: companies that don't invest in this kind of automation today risk ending up at a structural disadvantage within the next two to three years.

Rakuten's experience with Codex is strong evidence that the era of "AI as an assistant" is giving way to the era of "AI as a co-author of the engineering process." When a major corporation with billion-dollar infrastructure publicly reports a twofold speedup in fixing errors, the story is no longer about experiments — it is about a new industry standard. The question now is not whether to adopt tools like this, but how quickly the rest of the market is ready to do so.