Agentsway -- Software Development Methodology for AI Agents-based Teams

Human oversight remains central to AI-driven development: Agentsway emphasizes "human orchestration" as the core of its framework, meaning humans retain decision-making authority even as AI agents handle planning, coding, and testing tasks. This addresses concerns about autonomous systems operating without meaningful human control.

Specialized AI agents improve transparency and accountability: The framework assigns distinct roles to different agents (planning, coding, testing, fine-tuning) rather than using a single general-purpose AI. This specialization is designed to enable "explainable decision-making" and make it easier to understand why specific development choices were made—a key requirement for responsible AI systems.

Privacy and responsible AI are built into the development process: The paper explicitly integrates "responsible AI principles" through coordinated use of multiple fine-tuned models and emphasizes "privacy-preserving collaboration," suggesting that ethical considerations aren't added as an afterthought but are foundational to how these agent teams operate.

Continuous learning requires structured feedback mechanisms: Agentsway incorporates a "retrospective learning process" where agents learn from outputs and feedback throughout development. This structured approach to learning helps prevent AI agents from perpetuating errors and enables course correction—important for maintaining safety and quality as systems evolve.

Traditional management approaches need updating for AI collaboration: The paper argues that existing methodologies like Agile and Kanban are "increasingly inadequate" for teams including AI agents, suggesting organizations need new governance structures and workflows specifically designed for human-AI collaboration rather than adapting old frameworks.