Rakuten fixes issues twice as fast with Codex

2026-03-11 · Source: OpenAI News · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, short

Summary

Rakuten, a global innovation company with 30,000 employees, has integrated OpenAI's Codex into its engineering stack to accelerate software delivery and enhance reliability. This implementation has led to a 50% reduction in mean time to recovery (MTTR) for incident response and compressed development cycles from quarters to weeks. Rakuten uses Codex in operational workflows for KQL-based monitoring and diagnosis, in CI/CD pipelines for automated code review and vulnerability checks, and for autonomous development of complex projects, such as building a full-stack mobile app from a single specification. The company emphasizes that this approach prioritizes shipping safely alongside speed, shifting engineers' roles from writing code to defining clear specifications and verifying outputs.

Key takeaway

For CTOs and VPs of Engineering aiming to optimize software delivery, integrating AI coding agents like OpenAI's Codex can dramatically improve incident recovery times and accelerate project completion. You should explore deploying AI for automated code review, vulnerability checks, and even full-stack development from high-level specifications to free up engineering talent for higher-value verification tasks, ensuring both speed and security in your development pipeline.

Key insights

Integrating AI coding agents like Codex significantly boosts development speed, enhances safety, and fosters autonomous project execution.

Principles

• Speed without safety is not success.
• Shift engineering focus from writing to verifying.

Method

Rakuten uses Codex for KQL-based monitoring to accelerate root-cause analysis, integrates it into CI/CD for automated code review and vulnerability checks, and employs it for executing full-stack builds from ambiguous specifications.

In practice

• Reduce MTTR by ~50% with AI-assisted incident response.
• Automate code review in CI/CD with internal standards.
• Compress development cycles from quarters to weeks.

Topics

• OpenAI Codex
• Incident Response
• CI/CD Automation
• Software Development
• Automated Code Review

Best for: CTO, VP of Engineering/Data, Software Engineer, MLOps Engineer, Director of AI/ML

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• 🎧 How OpenAI’s Codex Team Uses Their Coding Agent — OpenAI's Codex is expanding its user base and capabilities, emphasizing speed and a dedicated GUI for enhanced developer experience.
• OpenAI Boosts Codex's Core Functionality — OpenAI's Codex updates enhance desktop control and multi-agent capabilities, directly challenging Anthropic's offerings.
• How OpenAI Codex Tech Lead Does AI-Assisted Engineering — A simple, iterative AI-assisted workflow, leveraging clear specs and small PRs, significantly enhances engineering efficiency.
• How Ramp engineers accelerate code review with Codex — AI tools like Codex can profoundly enhance developer productivity and code quality by automating complex tasks.

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Editorial summary, takeaway, and curation by AIssential. Original article published by OpenAI News.