Developer learns hard lesson on AI sub-agents after botched 500-script migration

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A developer attempting to migrate 500 legacy scripts to a structured logging engine initially deployed 10 parallel AI sub-agents to speed up the process, only to find the approach inefficient. The core problem was that each agent redundantly rediscovered file locations, decided on channel naming, and tracked its own progress — work better handled by deterministic tooling. The developer concluded that sub-agent parallelism adds value only when a problem branches into genuinely distinct reasoning paths, such as debugging across database, network, and application layers simultaneously. For repetitive, rule-based tasks like code migration, pre-generating inputs such as file locations and channel names and feeding them directly to a single agent proved far more effective. The experience highlighted a key distinction: many independent files do not automatically mean many independent reasoning problems.

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ProgrammingDEV Community ·

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