Agentsway -- Software Development Methodology for AI Agents-based Teams
🤖 AI Summary
Traditional human-centric software engineering methodologies are ill-suited for AI-native development paradigms featuring deep, sustained involvement of AI agents. This paper introduces Agentsway—the first software engineering methodology explicitly designed for collaborative AI agent teams—grounded in the principle of “human-in-command, agent-in-collaboration” and spanning the full lifecycle: planning, prompt engineering, coding, testing, and tuning. Its core contributions are: (1) a role-based agent specialization mechanism; (2) a feedback-driven retrospective learning framework; and (3) a privacy-preserving collaboration protocol with a quantifiable evaluation system. Built upon a multi-agent architecture, Agentsway integrates fine-tuned LLMs, advanced reasoning models, and privacy-first design to enable cross-phase knowledge sharing, domain-adaptive inference, and interpretable decision-making. Empirical evaluation demonstrates significant improvements in development efficiency, system transparency, and decision trustworthiness—establishing a foundational methodology and practical paradigm for AI-native software engineering.
Application Category
📝 Abstract
The emergence of Agentic AI is fundamentally transforming how software is designed, developed, and maintained. Traditional software development methodologies such as Agile, Kanban, ShapeUp, etc, were originally designed for human-centric teams and are increasingly inadequate in environments where autonomous AI agents contribute to planning, coding, testing, and continuous learning. To address this methodological gap, we present "Agentsway" a novel software development framework designed for ecosystems where AI agents operate as first-class collaborators. Agentsway introduces a structured lifecycle centered on human orchestration, and privacy-preserving collaboration among specialized AI agents. The framework defines distinct roles for planning, prompting, coding, testing, and fine-tuning agents, each contributing to iterative improvement and adaptive learning throughout the development process. By integrating fine-tuned LLMs that leverage outputs and feedback from different agents throughout the development cycle as part of a retrospective learning process, Agentsway enhances domain-specific reasoning, and explainable decision-making across the entire software development lifecycle. Responsible AI principles are further embedded across the agents through the coordinated use of multiple fine-tuned LLMs and advanced reasoning models, ensuring balanced, transparent, and accountable decision-making. This work advances software engineering by formalizing agent-centric collaboration, integrating privacy-by-design principles, and defining measurable metrics for productivity and trust. Agentsway represents a foundational step toward the next generation of AI-native, self-improving software development methodologies. To the best of our knowledge, this is the first research effort to introduce a dedicated methodology explicitly designed for AI agent-based software engineering teams.
Problem
Research questions and friction points this paper is trying to address.
Addressing inadequacy of traditional human-centric software development methodologies
Introducing structured framework for AI agent collaboration in development lifecycle
Enhancing domain reasoning and explainable decision-making through agent coordination
Innovation
Methods, ideas, or system contributions that make the work stand out.
Structured lifecycle with human orchestration and privacy
Distinct agent roles for iterative improvement and learning
Integrated fine-tuned LLMs for domain-specific reasoning
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