From Determinism to Delegation: AI-Native Software Engineering and the Evolution of the Agentic Engineer

Abstract

Software engineering is experiencing its most significant transformation since the emergence of high-level programming languages. As large language models (LLMs) increasingly enable sustained, multi-step, tool-mediated execution, engineering value is shifting from writing deterministic code to supervising probabilistic and autonomous behavior. This paper argues that AI-Native Software Engineering is a paradigm shift rather than a mere tooling advance, creating a new professional archetype: the Agentic Engineer, whose primary artifact is the agentic system rather than the program. We characterize this transition through three changes: (i) the unit of work shifts from functions to supervised agent workflows, (ii) correctness shifts from binary assertions to statistical evaluation under uncertainty, and (iii) accountability shifts from code authorship to outcome ownership. Drawing on post-2022 research, we compare traditional and agentic engineering roles and define core mechanisms of autonomous agents, including reasoning-acting loops, context engineering, tool use, memory, behavioral drift, and compositional error. We place human-AI collaboration within socio-technical frameworks and examine mixed empirical evidence. While some studies report productivity gains, others show slowdowns among experienced developers, highlighting disciplined oversight rather than automation as the critical competency. Using established governance frameworks, we identify required skills and risks, including indirect prompt injection. We conclude that the future is one of symbiosis rather than substitution: agentic engineering builds upon and depends on classical software engineering principles.