Rakuten fixes issues twice as fast with Codex

Rakuten⁠(opens in a new window) is a global innovation company operating across e-commerce, fintech, and mobile communications, serving both consumers and merchants at massive scale. With 30,000 employees worldwide, its engineering teams ship across a large, complex product ecosystem where both speed and reliability are essential.

That’s why Yusuke Kaji, General Manager of AI for Business at Rakuten, has spent the past year pushing agentic workflows deeper into how teams plan, build, and validate software. Codex⁠—the coding agent from OpenAI—has become a core part of Rakuten’s engineering stack, especially where the company needs to move faster without compromising security.

Over the past year, Rakuten engineers have used Codex across operations and software delivery to compress incident response (including a ~50% reduction in mean time to recovery, or MTTR), strengthen CI/CD with automated code review and vulnerability checks, and support more autonomous development on complex projects.

“We don’t just care about generating code quickly,” Kaji says. “We care about shipping safely. Speed without safety is not success.”

Inside Rakuten’s engineering team, their AI agenda is crisp and intentionally operational. Kaji frames the work around three priorities that teams rally behind:

• Build faster (“Speed!! Speed!! Speed!!”): Teams use Codex in operational workflows, including KQL-based monitoring and diagnosis, to accelerate root-cause analysis and remediation, helping compress MTTR by up to 50%.
• Build safer (“Get things done”): Codex is invoked in CI/CD for code review and vulnerability checks, applying internal standards automatically so teams can ship quickly with guardrails.
• Operate smarter (“AI-nization”): Codex drives larger, ambiguous projects forward from specification toward working implementations, reducing dependence on perfectly-defined requirements, enabling more autonomous execution, and ultimately compressing quarter-long efforts into weeks.

Codex maps directly to each priority as a dependable agent in a broader toolkit, showing up where speed, safety, and autonomy create compounding value.

Speed at Rakuten includes recovery time, not just development velocity.

Teams use KQL (Azure’s query system for logs and telemetry) to monitor APIs and analyze signals. Codex works alongside these workflows to help identify root causes and suggest fixes, reducing the time between alert and resolution.

From a site reliability engineering (SRE) perspective, this shortens the path from detection to remediation. Instead of manually stitching together queries, logs, and patches, engineers can focus on validating and deploying fixes.

Rakuten estimates this approach can reduce MTTR by approximately 50% when issues occur. Or more simply put: Rakuten has used Codex to fix problems twice as fast when something breaks.

As shipping accelerates, review and deployment can become bottlenecks. Rakuten addresses this by integrating Codex directly in its CI/CD pipeline.

Codex conducts code review and vulnerability checks before changes reach production. Rakuten feeds internal coding principles and standards into these workflows so reviews align with company expectations.

“We provide our internal coding principles to Codex,” Kaji says. “Using the same principles, it reviews whether the code aligns with our standards.”

The result: safety checks happen consistently and automatically, enabling teams to move faster without lowering standards.

Rakuten’s third priority—AI-nization—focuses on autonomy. Codex is used not only for review and maintenance, but also for executing larger, ambiguous projects end-to-end. Instead of requiring perfectly defined specifications, Codex can move forward from partial requirements and produce usable artifacts.

“The latest Codex models can read between the lines,” Kaji says. “Even if the requirements are not perfectly defined, it understands what we’re trying to build.”

One example: building a mobile app version of an existing web-based AI agent service. Codex implemented the entire specification, involving a full stack implementation with a Python/FastAPI backend and a Swift/SwiftUI iOS app, including all the backend APIs, without step-by-step human instruction. Codex cut the development time for this project from one quarter to weeks.

As Codex takes on more code generation work, Rakuten is shifting the engineer’s role to writing clearer specifications and verifying outputs against measurable standards. 

“Our role is not to check every line of code anymore,” Kaji says. “Our role is to define clearly what we want and establish how to verify it.”

Rakuten has supported this shift through hands-on workshops across engineering, product, and non-technical teams—contributing to Codex playing a central role in helping teams ship faster, operate more safely, and scale autonomous development across the organization.