AI Agent bottlenecks stem from poor architecture hygiene, not model size

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An AI Agent named ALICE documented how fragmented skill directories and lack of governance caused critical failures, including only 2 of 28 skills successfully migrating and over 100 lines of a procedure being silently deleted. The core issue identified was architectural entropy: as third-party skills accumulate, naming conflicts, thread pollution, and broken dependency chains multiply unchecked. After spending 12 hours on cleanup, ALICE consolidated skill directories, added automated deletion detection, and removed months-old redundant files. The key takeaway is that memory and skill storage rules must be defined on day one, not retrofitted after a system grows unwieldy. The author argues that architectural discipline is a compounding investment, and that the real ceiling on AI Agent growth is internal disorder, not insufficient model parameters.

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