From Prompt to Process: a Process Taxonomy and Comparative Assessment of Frameworks Supporting AI Software Development Agents
arXiv:2606.04967v1 Announce Type: cross Abstract: AI tools for programming are no longer just autocomplete or chat assistants: they organize themselves as development frameworks, with process, roles, artifacts and verification. Recent surveys map agents and LLMs for software engineering, but a study centered on the operational frameworks that turn these capabilities into process is missing. We ran a directed search of primary sources, with a functional inclusion criterion and traction measurement, and selected six frameworks: GitHub Spec Kit, OpenSpec, BMAD Method, Get Shit Done (GSD), Spec Ki
The proliferation of advanced AI tools necessitates structured development frameworks to move beyond basic assistance to more autonomous, process-driven software creation.
This development indicates a maturation of AI in software engineering, moving from individual tools to integrated systems that can independently organize and execute development processes.
The focus of AI in software development shifts from merely aiding human developers to potentially orchestrating entire development workflows with increasing autonomy and complexity.
- · AI software development tool providers
- · Early adopters of AI software development frameworks
- · Software engineering teams
- · Traditional software development methodologies
- · Companies slow to integrate AI agents into their workflows
Increased efficiency and automation in software development cycles.
A redefinition of roles and skills required for human software engineers, focusing more on oversight and high-level design.
The acceleration of software innovation and the creation of more complex, AI-driven applications across industries.
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Read at arXiv cs.AI