With the introduction of AI tools in workflows (GitHub Copilot, CodeWhisperer, DeepCode, etc.), even code review has become automatable. AI can quickly analyze code and offer suggestions, alerts, and automatic approvals.

✅ The Advantages Are Evident

• Speed: PR approval times are significantly reduced
• Automation of repetitive checks: naming conventions, duplicate code, known vulnerabilities, etc.
• Support for junior developers: AI can suggest improvements in real-time
• Guided refactoring: AI can propose cleaner or more performant solutions

However, alongside these benefits, important limitations emerge that need to be considered.

⚠️ The Invisible Limit: The Loss of Shared Knowledge

Entrusting PR approval to AI means bypassing human interaction. No colleague reads that code, no one understands its intent, no one learns from its logic or implementation choices. This leads to two main risks:

1. Loss of Knowledge Sharing

Those who approve automatically learn nothing, those who propose the code receive no feedback. In an Agile team, where the focus is on peer collaboration and collective growth, this is a critical problem.

2. Superficial Validation

AI analyzes form, but not always context or business logic. Code can be “clean” but wrong in behavior. Only a human reviewer can grasp these nuances.

👥 Comparison with Pair Programming

To understand the impact of this loss, just compare it with another Agile practice: pair programming.

In pair programming, two developers work together on the same code: one writes (driver), the other observes and guides (navigator). Roles alternate, fostering discussion, real-time review, and knowledge sharing.

Pull Requests, in asynchronous form, offer a similar space for technical dialogue. Completely automating them with AI means eliminating a moment of shared review, which enriches both the code and the people who write it.

🧠 How to Integrate AI Without Losing Agile Values

AI shouldn’t be excluded: it can be a valuable resource, if used the right way. The key lies in intelligent integration, not substitution.

🎯 Recommended Approach

1. Automated pre-review with AI: AI flags obvious issues, suggests refactoring, runs tests, and performs linting
2. Final human review: the team reads, discusses, approves, and learns
3. AI intervention logs: every change proposed or approved by AI must be visible and traceable

This way you get the best of both worlds: the efficiency of automation and the human value of collaboration.

✅ Conclusion

Artificial intelligence can accelerate code review, increase productivity, and reduce errors. But in the Agile context, it cannot replace human collaboration. Pull Requests are a precious moment of alignment, learning, and growth.

Letting AI approve our code is convenient. But letting AI approve our way of collaborating? On this, we still need a human being.