Developer Finds AI-Deterministic Software Hybrid Hits Hidden Complexity Ceiling

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A developer spent roughly a month building hybrid tools that paired deterministic scripts with large language models to automate tasks around their book-publishing work. The system gradually evolved from simple scripts into a structured, multi-department setup resembling a small company with specialised AI workers. To manage growing context challenges, the developer experimented with sub-agents, MCP servers, and task-driven development to improve how information was retrieved and passed between components. However, a fundamental bottleneck emerged: autonomous agents must independently acquire, interpret, and act on information, making the probabilistic layer computationally expensive rather than free. The author concluded that scaling such hybrid systems requires accounting not just for what AI agents can do, but for the hidden cost of enabling them to understand and act on their own.

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