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One of the primary drivers behind the shift from Waterfall to Agile was the sheer inefficiency of trying to freeze requirements. Requirements are in a constant state of flux; they evolve and are discovered anew. During the implementation phase, unforeseen challenges inevitably emerge, forcing us to incorporate elements into the specifications that were never previously considered. This phenomenon intensifies with the scale of the software. It’s the same logic as a long vector: even a minuscule shift in angle results in a massive displacement at the tip.

In the era of AI agents, problem-solving skills are no longer paramount, as AI will solve everything for us. Nevertheless, higher education remains centered around teaching problem-solving skills. Exams are a prime example of this. Excelling at solving problems does not necessarily mean one is good at managing others. You have likely heard of cases where an outstanding worker gets promoted to a management role, only to become a terrible manager.

While multi-agent programming solves context limitations and enables complex tasks, it introduces a critical risk of race conditions. Agents operating on stale contexts may generate incorrect patches or overwrite each other’s changes. This conflict often results in infinite modification loops as agents repeatedly attempt to fix errors introduced by concurrent edits on intertwined code. Consequently, the efficiency of multi-agent programming is seriously degraded. To address this problem, we propose a reader-writer lock mechanism to prevent race conditions.