An AI-native SDLC is about integrating AI across the entire software development lifecycle, from requirements and architecture to testing, deployment, and maintenance. The idea sounds promising, but I’m curious about how well it works in real-world engineering environments.

Writing code is only one part of software development. Understanding legacy systems, handling dependencies, debugging complex issues, maintaining security, and validating releases can still take considerable time.

I’m particularly interested in how teams are approaching AI agents that can execute development tasks rather than simply suggest code.

A few questions for those working in software engineering:

Are you already using AI across multiple SDLC stages, or mainly for coding and testing?

What has been the biggest challenge when integrating AI into existing development workflows?

Do you think AI-native SDLC can genuinely reduce delivery time without compromising code quality and security?

Would love to hear about your experiences, especially from teams working on enterprise applications and complex codebases.