How AI Code Reviews are Revolutionizing Software Reliability at Datadog
Published: 10 Jan 2026
Using Artificial Intelligence (AI) in code reviews is changing how companies like Datadog catch potential problems in software before they affect users. AI helps detect risks that human reviewers may miss, especially in difficult systems.
The Challenge: Balancing Speed and Reliability
For companies like Datadog, finding the right balance between quick software deployment and keeping systems stable is critical. Datadog helps clients monitor and fix problems in their software systems. So, it’s important that their code is reliable even before it goes live.
The main tool for ensuring reliability has been code review, where engineers check for errors before software is deployed. But as teams grow, relying on human reviewers to catch every problem becomes harder.
That’s where AI comes in. Datadog used OpenAI’s Codex to automate this process, helping them catch errors faster and more effectively.
Why Old Code Review Tools Don’t Always Work
In the past, automated tools helped find small mistakes in code, like missing commas or bad formatting. But these tools couldn’t look at the bigger picture. They didn’t understand how changes in one part of the system could affect other parts.
Datadog needed something better. They wanted a tool that didn’t just look for syntax errors but could also understand how different parts of the system interact. This is why they turned to Codex.
How AI Code Reviews Work at Datadog
Datadog now uses AI to review every change made to their code. This AI checks each pull request and compares it with what the developer meant to do. It runs tests to see if the code will work as expected and spot potential issues before they become bigger problems.
Instead of just checking for small errors, the AI can look at how the change might impact the whole system. This helps catch problems that might not be obvious at first.
Proving AI’s Value: Testing Against Past Failures
To show the value of AI, Datadog tested it using past mistakes. They took pull requests that had caused problems before and ran them through the AI tool to see if it would have caught the issues.
The results were impressive: the AI spotted 22% of the problems that humans missed. This showed that AI could help prevent future issues by finding errors that were overlooked before.
How AI is Changing the Way Engineers Work
By using AI, Datadog has changed how their engineers work. The AI doesn’t replace human reviewers but supports them. Engineers now spend less time looking for small bugs and more time focusing on system design and architecture.
The AI also helps catch problems that are hard to spot, like missing tests in areas where different parts of the system interact. This makes the code review process much more effective.
The Bottom Line: AI Boosts Software Reliability
Datadog’s experience shows how AI can improve software reliability. Code reviews are no longer just about finding bugs, they’re about making sure the code will work well in the long term. By catching issues early, AI helps engineers feel more confident about shipping new features.
This shift means that Datadog’s systems are more stable, and their customers trust them to keep things running smoothly, even when other platforms might fail.
Key Takeaways
- AI in Code Review: AI can detect risks that human reviewers might miss, making code more reliable.
- Incident Prevention: Datadog showed that AI can prevent up to 22% of errors that slip past human reviewers.
- Efficiency and Focus: Engineers can focus on big-picture issues, while AI handles smaller, tricky problems.
- Improved Reliability: Using ai helps Datadog maintain trust with their customers by ensuring their software runs smoothly
- Be Respectful
- Stay Relevant
- Stay Positive
- True Feedback
- Encourage Discussion
- Avoid Spamming
- No Fake News
- Don't Copy-Paste
- No Personal Attacks
- Be Respectful
- Stay Relevant
- Stay Positive
- True Feedback
- Encourage Discussion
- Avoid Spamming
- No Fake News
- Don't Copy-Paste
- No Personal Attacks
