What This Is
This is not the release of a new tool, but a record of an AI software development workflow: a lead agent—the AI responsible for advancing the task—first reads the requirements document, architecture decisions, and codebase; generates a technical solution; and then breaks that solution into multiple parallelizable subtasks for subagents—separate AIs responsible for individual parts—to implement. The core objective is to make the development stage something where there is “no need to guess”: every requirement has an owner, dependencies, a testing method, and explicit authorization.
The technical solution is generated only once and manually reviewed by a developer. Tasks are then divided by user-visible behavior rather than simply along platform or stack lines such as Android, iOS, frontend, and backend. Neither passing automated checks nor an AI deciding that a solution is sound can replace explicit confirmation from a developer.
Industry View
We have noticed that this approach moves AI from “the person who writes code” upstream to “the person who organizes complex delivery conditions.” When requirements, specifications, and the codebase can be maintained continuously, AI’s value lies not just in increasing coding speed, but also in reducing omissions, rework, and waiting across teams. This is particularly valuable for enterprise IT—the internal technology systems used by businesses—because the technical solution, task graph, and acceptance-criteria mapping create a traceable record.
The risks are equally clear: the more a workflow depends on documentation and the code baseline, the more likely AI is to package faulty premises into a tightly argued solution when the source material has become inaccurate. If the development scope is broken down too finely, coordination, merging, and testing costs also rise. Issues that appear to be technical details—such as cancellation behavior, timeouts, and notification order—still require confirmation from the business owner whenever they change user behavior. The four Mermaid diagrams mentioned in the article—a chart syntax that describes system structures and flows in text—can also create an illusion of “complete design” if they are detached from the actual code.
Generating the solution once and reviewing it manually also sacrifices some of the speed offered by automatic iteration. Reviewers need enough time to understand the solution; otherwise, the process merely replaces “multiple rounds of AI debate” with “one human reviewing a consolidated document.” It is therefore better suited to mature teams with relatively stable requirements, and should not be applied directly to high-risk or frequently changing projects.
Impact on Regular People
For enterprise IT: AI coding tools are moving upstream into requirements decomposition, architecture design, acceptance, and other early-stage activities. When enterprises evaluate these tools, they should consider whether they can integrate with existing codebases, leave audit records, and explicitly preserve human release authority.
For individual careers: A developer’s most important skill may shift from writing code line by line to defining requirements, identifying boundaries, and judging solution quality. People who understand the business and can clearly articulate what “done” means will have greater bargaining power.
For the consumer market: In the short term, AI will not immediately make all software cheaper or deliver a consistent improvement in user experience. It is more likely to affect delivery cycles, test coverage, and the costs of small and midsize teams first. The ultimate outcome will still depend on whether companies are willing to redesign their processes alongside adopting the tools, rather than simply buying them.