AI can generate code faster than ever, but getting it tested and reviewed and into production is a different challenge.

Engineering teams are increasingly using AI agents to write code, fix bugs, and build features. But faster coding does not necessarily mean faster software delivery.

As code generation accelerates, reviews pile up, testing slows down, and teams struggle to coordinate the growing volume of changes.

At TechSparks 2026, Tushar Mathur, CEO of Forge Code, explored this problem in his session, Anatomy of a software factory. He explained how AI-native engineering organisations can move beyond automating individual coding tasks to building systems that manage the entire software development lifecycle.

“Just because code is becoming easy and faster to generate doesn't mean your overall SDLC becomes faster,” Mathur said.

The traditional software development process focused heavily on writing code efficiently. Teams spent time defining requirements, preparing specifications and designing architecture to reduce the effort needed during implementation, he said.

With AI taking over more coding work, the bottlenecks are moving elsewhere.

Mathur described how organisations can end up with large volumes of pull requests awaiting review, longer testing cycles and more bugs than existing regression suites can catch.

Hiring more engineers or running more AI agents is not necessarily the answer. More people can increase coordination costs, while adding agents without effective feedback loops can generate code that overwhelms the systems responsible for reviewing and testing it.

“The reason why this happens is because there's no signal,” Mathur said, explaining why simply connecting automated steps does not create an effective software factory.

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