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AI agents aren't replacing software engineering but expanding it far beyond code, researchers argue
The popular story goes that AI agents are swallowing more programming work and developers are headed for obsolescence. A new paper from researchers at Chalmers University of Technology and the Volvo Group argues that view misses the point.
The researchers offer a different take: agent-based AI systems expand software engineering with what they call "semi-executable artifacts." These include prompts, workflows, policies, escalation rules, and decision routines. They shape system behavior just as directly as code, but they rely on human or probabilistic interpretation to actually run.
Six rings instead of just code
At the heart of the paper is the "Semi-Executable Stack," a diagnostic model built from six rings. At the center sits classic code as ring 1, followed by prompts and natural language specifications as ring 2, and orchestrated agent workflows as ring 3. Ring 4 covers control systems like guardrails and monitoring. Ring 5 represents operational organizational logic, such as decision-making routines. Ring 6 captures the social and institutional fit, including frameworks like the EU AI Act.
The authors point out that software engineering has historically focused on rings 1 and 2. Now, rings 2 through 5 are turning into high-priority engineering objects, and ring 6 increasingly decides what actually works in practice.
The biggest gap, according to the researchers, sits in the outer rings 5 and 6. Engineering methods for code have existed for decades, but equivalents for decision routines, governance, and institutional fit are still missing. Most research continues to concentrate on code generation, bug-fixing, testing, and benchmarks in rings 1 through 3.
The researchers back their argument with three observations: first, AI doesn't need to match the best engineer to change how teams work; it just has to be good enough. Second, scale matters more than peak performance. Many small, everyday AI deployments deliver more value to an organization than rare access to a top expert. Third, as more domain experts build their own systems using natural language, the need for clean engineering practices grows rather than shrinks.