# Agentsway -- Software Development Methodology for AI Agents-based Teams arXiv: https://arxiv.org/abs/2510.23664 ยท HTML: https://mlsanity.com/paper/2510.23664/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. - Builds on 11 of its 34 references that are in the map. - First posted on arXiv 26 Oct 2025. ## Placed in - LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/) ## Builds on (most cited first, 11 of 11) - [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024) - [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024) - [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://mlsanity.com/paper/2403.13372/) (2024) - [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025) - [Vision-Language Models for Vision Tasks: A Survey](https://mlsanity.com/paper/2304.00685/) (2023) - [Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes](https://mlsanity.com/paper/2304.09433/) (2023) - [LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models](https://mlsanity.com/paper/2404.01230/) (2024) - [Human-In-the-Loop Software Development Agents](https://mlsanity.com/paper/2411.12924/) (2024) - [On the Use of Agentic Coding Manifests: An Empirical Study of Claude Code](https://mlsanity.com/paper/2509.14744/) (2025) - [Context Engineering for Multi-Agent LLM Code Assistants Using Elicit, NotebookLM, ChatGPT, and Claude Code](https://mlsanity.com/paper/2508.08322/) (2025) - [To Agile, or not to Agile: A Comparison of Software Development Methodologies](https://mlsanity.com/paper/1704.07469/) (2017) ## Built on by (most cited first, 14 of 14) - [A Practical Guide for Designing, Developing, and Deploying Production-Grade Agentic AI Workflows](https://mlsanity.com/paper/2512.08769/) (2025) - [Towards Responsible and Explainable AI Agents with Consensus-Driven Reasoning](https://mlsanity.com/paper/2512.21699/) (2025) - [ASTRIDE: A Security Threat Modeling Platform for Agentic-AI Applications](https://mlsanity.com/paper/2512.04785/) (2025) - [Security in the Age of AI Teammates: An Empirical Study of Agentic Pull Requests on GitHub](https://mlsanity.com/paper/2601.00477/) (2026) - [A Practical Guide to Agentic AI Transition in Organizations](https://mlsanity.com/paper/2602.10122/) (2026) - [AI Trust OS -- A Continuous Governance Framework for Autonomous AI Observability and Zero-Trust Compliance in Enterprise Environments](https://mlsanity.com/paper/2604.04749/) (2026) - [Flowr -- Scaling Up Retail Supply Chain Operations Through Agentic AI in Large Scale Supermarket Chains](https://mlsanity.com/paper/2604.05987/) (2026) - [Toward Zero-Egress Psychiatric AI: On-Device LLM Deployment for Privacy-Preserving Mental Health Decision Support](https://mlsanity.com/paper/2604.18302/) (2026) - [Think Before You Act -- A Neurocognitive Governance Model for Autonomous AI Agents](https://mlsanity.com/paper/2604.25684/) (2026) - [Towards an Agent-First Web: Redesigning the Web for AI Agents](https://mlsanity.com/paper/2606.19116/) (2026) - [Scaling Human-AI Coding Collaboration Requires a Governable Consensus Layer](https://mlsanity.com/paper/2604.17883/) (2026) - [Train the Trainers -- An Agentic AI Framework for Peer-Based Mental Health Support in Battlefield Environments](https://mlsanity.com/paper/2605.16269/) (2026) - [Human-AI Agent Interaction as a Neuroplastic Training Environment](https://mlsanity.com/paper/2607.12823/) (2026) - [BaseCamp --- An Agentic AI Framework for Automating DNA Sequencing Data Pipelines](https://mlsanity.com/paper/2609.28557/) (2026) ## Abstract (arXiv) 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.