The Engineering Behind An AI-Native Software Development Life Cycle

Sergey Marchuk is CTO & Co-Founder at Oxagile, a software engineering company enhancing online video experiences with AI.

​Agents have lowered the cost of producing code, but writing code was never the whole job.

A production feature still needs someone to figure out what it should do, make architectural decisions, break the work down, test the result, review it and take responsibility when something goes wrong. Give an agent a vague Jira ticket and ask it to code, and you’ll still have to solve all of those problems. ​

In other words, even as implementation gets cheaper, ambiguity in the requirements, weak architectural decisions and gaps in verification can cost you more than before. ​

For example, as reported by Ars Technica, a recent study from Harvard University researchers found that introducing coding agents leads to a 30% increase in total lines of code generated, but that the time between a pull request being submitted and the code being merged into the codebase rose 49%. ​​

The distinction I find useful is between giving developers AI coding tools and building an engineering operating model around agents.

As the CTO of a software development and consulting company, I’m seeing the results firsthand. My team is still experimenting with what works, and I don’t think anyone can credibly claim yet that the industry has settled on one model, but I’m seeing several patterns becoming clear.

The SDLC doesn’t disappear, but the work inside it moves

Requirements, architecture, decomposition, implementation, review, testing. AI didn’t break or reinvent the software development life cycle, but human effort is being redistributed inside it. ​

In my experience, writing code by hand becomes an exception in an AI-native SDLC. Instead, engineers spend more time on interfaces, constraints, architectural decisions and decomposition. My team breaks work into atomic tasks with their own completion criteria, small enough to finish inside a single clean agent context.