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The Coverage Problem: Why LLMs Can't Do Matrix Math

LLM limitations are not about intelligence — they are about guarantees. A large language model can produce a plausible answer for almost any single input you hand it. What it cannot do is guarantee correctness across the full combinatorial space of inputs and conditions that a real production system must handle. That gap — between "works on the inputs I tested" and "works on every input that will ever arrive" — is the coverage problem.

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Custom AI Agents: Build vs. Buy — A Decision Guide for 2026

Short answer: Buy an off-the-shelf agent when the job is generic and the data is standard — you'll be live in weeks. Build custom when the agent runs on your proprietary data and workflows and becomes a competitive moat. In practice, most businesses land on a managed middle path: custom where it differentiates, bought where it doesn't, with a partner running both.