ai-automation

Three API calls per perspective for six perspectives. Half a minute of waiting. Complete agreement. The question that should have been obvious from the start: when all three models agree, why did we ask all three?

Collect over 150 citations across eight agents. Use barely 30 in the final synthesis. Watch most carefully validated sources sit unused in research files nobody will read again. This is about the cost of thoroughness nobody mentions.

Six sessions of feature additions. Nobody checked the cumulative cost. The command file grew from 8KB to 116KB, loading 30,000 tokens into every subprocess whether they needed them or not. The solution wasn't adding more. It was splitting what was already there.

Six days of building, and the first real test actually worked. Eight perspectives, quality scores in the 90s, hundreds of sources validated. Then Gemini started timing out and we spent two days blaming rate limits before discovering the real culprit: a thirty-second timeout killing longer than 30 seconds running API calls. Sometimes the bug isn't in the vendor's system. Sometimes it's a number you typed six months ago.

Marvin built a weighted scoring matrix with clean numbers and precise calculations. Claude +3 for technical, Grok +3 for X. It looked scientific. Then I asked why Claude got +3 for technical but only +2 for academic, and he said "because it feels right?" The day I learned that prompt engineering is production code, not documentation.

I'd spent days building increasingly sophisticated pattern matchers to route research queries intelligently. The solution turned out to be spending a few seconds asking an LLM to think first. Sometimes the problem isn't that you don't know the answer. It's that you're too stubborn to use it.