The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE

2026-04-16

AI Summary

This work addresses the disruptive impact of AI agent systems on traditional software engineering paradigms, arguing for a redefinition of their boundaries and core artifacts. The paper proposes a six-layer “semi-executable stack” reference model that extends software artifacts beyond purely executable code to include semi-executable elements—such as natural language specifications, toolchains, and control mechanisms—that require human interpretation or probabilistic execution. Complementing this model, a diagnostic framework is introduced to systematically characterize the evolutionary trajectory of software engineering in the AI era. Through conceptual modeling, case studies, and a reconstruction of engineering philosophy, the study formulates a “retain-versus-purify” heuristic strategy to guide practitioners in identifying contributions, recognizing bottlenecks, and navigating organizational transitions, thereby offering theoretical grounding for rational technological transformation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent/AI Theories and Architectures
• Philosophy and Ethics of AI: Philosophical Foundations of AI

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

AI-based systems, currently driven largely by LLMs and tool-using agentic harnesses, are increasingly discussed as a possible threat to software engineering. Foundation models get stronger, agents can plan and act across multiple steps, and tasks such as scaffolding, routine test generation, straightforward bug fixing, and small integration work look more exposed than they did only a few years ago. The result is visible unease not only among students and junior developers, but also among experienced practitioners who worry that hard-won expertise may lose value. This paper argues for a different reading. The important shift is not that software engineering loses relevance. It is that the thing being engineered expands beyond executable code to semi-executable artifacts; combinations of natural language, tools, workflows, control mechanisms, and organizational routines whose enactment depends on human or probabilistic interpretation rather than deterministic execution. The Semi-Executable Stack is introduced as a six-ring diagnostic reference model for reasoning about that expansion, spanning executable artifacts, instructional artifacts, orchestrated execution, controls, operating logic, and societal and institutional fit. The model helps locate where a contribution, bottleneck, or organizational transition primarily sits, and which adjacent rings it depends on. The paper develops the argument through three worked cases, reframes familiar objections as engineering targets rather than reasons to dismiss the transition, and closes with a preserve-versus-purify heuristic for deciding which legacy software engineering processes, controls, and coordination routines should be kept and which should be simplified or redesigned. This paper is a conceptual keynote companion: diagnostic and agenda-setting rather than empirical.

Problem

• Semi-Executable Stack
• Agentic Software Engineering
• Foundation Models
• Software Engineering Scope
• AI in Software Development

Innovation

• Semi-Executable Stack
• Agentic Software Engineering
• Foundation Models
• Software Engineering Evolution
• Human-AI Collaboration