You ask the agent to count how many records match, or to reformat two hundred entries, or to total a column. It answers — confidently, in prose. It looks right. It's off by three, because a language model didn't count: it estimated, the way you'd estimate a jar of marbles from across the room.
There is a clean line between two kinds of work. One has a single correct answer a machine can compute: counting, parsing a fixed format, transforming data, arithmetic, lookups. The other needs judgement: what to build, how to word it, which trade-off to take. Models are extraordinary at the second and quietly unreliable at the first — so the rule routes each kind to the tool that is actually good at it:
✕ Without the rule
✓ With the rule
Drop it into your CLAUDE.md. It won't slow down the work that genuinely needs reasoning — that's most of it. What it catches is the quiet failure: the confident wrong count, the missed row, the entry silently dropped in a reformat. The tell is structure. If the input has a consistent shape, the answer belongs to a parser, not a paragraph.