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ZENODO
Journal . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Journal . 2026
License: CC BY
Data sources: Datacite
ZENODO
Journal . 2026
License: CC BY
Data sources: Datacite
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AI-First Software Development Lifecycle: An Agent-Driven Framework for Autonomous Planning, Coding, Testing, and Deployment

Authors: Ambar Nath Saha; Debashis Patra;

AI-First Software Development Lifecycle: An Agent-Driven Framework for Autonomous Planning, Coding, Testing, and Deployment

Abstract

In the era of automation, society has invested significant effort in automating repetitive processes across various sectors to reduce the manufacturing time of many products. However, we have not given similar attention to automating software development, as it involves complex decision-making, contextual understanding, and requires human expertise and coordination. Historically, most organizations followed the waterfall methodology for the Software Development Life Cycle (SDLC), and in the early 21st century, they rapidly adopted agile methodologies with the expectation of delivering more robust and scalable products within a shorter timeframe. However, human involvement has remained central in all these methodologies until the emergence of Agentic AI. Agentic AI has the potential to transform software development in ways that have not been previously explored. In this paper, we propose an agent-driven SDLC framework that adopts an AI-first approach to software development, where human involvement is limited to governance and decision-making. The framework introduces a Central Orchestrator Agent that coordinates with specialized agents responsible for backlog planning, solution architecture, code generation, automated testing, code review, CI/CD, deployment orchestration, and production monitoring with self-healing capabilities. This AI-first approach can significantly reduce human effort and software release time, maintain high code quality through automated validation, and enable rapid incident response through autonomous hotfix generation and rollback mechanisms.

Keywords

Large Language Models, LLM Guardrails, CI/CD Automation, Autonomous Software Engineering, Multi-Agent Orchestration, Software Development Lifecycle, AI Agents, Self-Healing Systems

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average