A four-dimension classifier routes agentic coding tasks into HITL, human-over-the-loop, or automated-with-monitoring tiers, analytically estimated to keep ~91% of ungoverned coding velocity under regulatory constraints.
Rethinking Software Engineering for Agentic AI Systems
4 Pith papers cite this work. Polarity classification is still indexing.
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abstract
The rapid proliferation of large language models (LLMs) and agentic AI systems has created an unprecedented abundance of automatically generated code, challenging the traditional software engineering paradigm centered on manual authorship. This paper examines whether the discipline should be reoriented around orchestration, verification, and human-AI collaboration, and what implications this shift holds for education, tools, processes, and professional practice. Drawing on a structured synthesis of relevant literature and emerging industry perspectives, we analyze four key dimensions: the evolving role of the engineer in agentic workflows, verification as a critical quality bottleneck, observed impacts on productivity and maintainability, and broader implications for the discipline. Our analysis indicates that code is transitioning from a scarce, carefully crafted artifact to an abundant and increasingly disposable commodity. As a result, software engineering must reorganize around three core competencies: effective orchestration of multi-agent systems, rigorous verification of AI-generated outputs, and structured human-AI collaboration. We propose a conceptual framework outlining the transformations required across curricula, development tooling, lifecycle processes, and governance models. Rather than diminishing the role of engineers, this shift elevates their responsibilities toward system-level design, semantic validation, and accountable oversight. The paper concludes by highlighting key research challenges, including verification-first lifecycles, prompt traceability, and the long-term evolution of the engineering workforce.
years
2026 4representative citing papers
Proposes GATF framework integrating governance controls into autonomous testing and reports 89.6% governance risk reduction plus 94.3-96.5% accuracy/reliability metrics on Defects4J and PROMISE datasets.
This interpretive synthesis maps three coexisting software engineering paradigms (Traditional, Generative AI-Enabled, Agentic AI-Enabled), proposes a five-category competency framework, and derives nine testable propositions about the shift toward human-AI collaboration.
Agentic Agile-V uses Agile-V as backbone and a Specify-Constrain-Orchestrate-Prove-Evolve-Verify loop to convert AI agent conversations into traceable engineering artifacts with acceptance evidence.
citing papers explorer
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Governed AI-Assisted Engineering: Graduated Human Oversight for Agentic Code Generation in Regulated Domains
A four-dimension classifier routes agentic coding tasks into HITL, human-over-the-loop, or automated-with-monitoring tiers, analytically estimated to keep ~91% of ungoverned coding velocity under regulatory constraints.
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Governance Controls for AI-Generated Test Artifacts in Autonomous Software Testing
Proposes GATF framework integrating governance controls into autonomous testing and reports 89.6% governance risk reduction plus 94.3-96.5% accuracy/reliability metrics on Defects4J and PROMISE datasets.
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Human-AI Collaboration and the Transformation of Software Engineering Work
This interpretive synthesis maps three coexisting software engineering paradigms (Traditional, Generative AI-Enabled, Agentic AI-Enabled), proposes a five-category competency framework, and derives nine testable propositions about the shift toward human-AI collaboration.
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Agentic Agile-V: From Vibe Coding to Verified Engineering in Software and Hardware Development
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