Agentsway -- Software Development Methodology for AI Agents-based Teams
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911235163815936 |
|---|---|
| author | Bandara, Eranga Gore, Ross Liang, Xueping Rajapakse, Sachini Kularathne, Isurunima Karunarathna, Pramoda Foytik, Peter Shetty, Sachin Mukkamala, Ravi Rahman, Abdul Hass, Amin Keong, Ng Wee De Zoysa, Kasun Withanage, Aruna Loganathan, Nilaan |
| author_facet | Bandara, Eranga Gore, Ross Liang, Xueping Rajapakse, Sachini Kularathne, Isurunima Karunarathna, Pramoda Foytik, Peter Shetty, Sachin Mukkamala, Ravi Rahman, Abdul Hass, Amin Keong, Ng Wee De Zoysa, Kasun Withanage, Aruna Loganathan, Nilaan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23664 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Agentsway -- Software Development Methodology for AI Agents-based Teams Bandara, Eranga Gore, Ross Liang, Xueping Rajapakse, Sachini Kularathne, Isurunima Karunarathna, Pramoda Foytik, Peter Shetty, Sachin Mukkamala, Ravi Rahman, Abdul Hass, Amin Keong, Ng Wee De Zoysa, Kasun Withanage, Aruna Loganathan, Nilaan Software Engineering Artificial Intelligence 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. |
| title | Agentsway -- Software Development Methodology for AI Agents-based Teams |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2510.23664 |