An IBM engineer explained agent harnesses in 20 minutes at @aiDotEngineer, and it's the best tutorial you'll find anywhere. Tejas Kumar gave GPT-3.5 one job, upvoting a Hacker News post. The agent hit a login screen and still reported success, and he fixed it without changing the prompt. People are paying $500 for agent courses that teach less than this. Watch it, then read the article on the slow death of the enterprise below.

The smartest person in your company is your biggest bottleneck. A PE operating partner introduced us to a medical billing company that processes claims for hundreds of healthcare facilities. The co-founder spent roughly ten hours a week writing queries against a 20-year-old database to produce financial health assessments for prospects. No dashboard could replace her. Dashboards answer the questions someone already thought to measure. She answered the ones nobody anticipated: ad-hoc queries against prospect data that changed with every deal. The operating partner saw it before our first call. Key-person dependency on the co-founder, manual analytical processes consuming executive hours, and a leadership team that knew the problem but couldn't solve it internally. We put one AI Velocity Pod on it: one senior AI engineer, a fractional AI lead, and a fractional delivery lead. We sat with the co-founder for three weeks before writing any agent logic. Denial definitions, remittance joins, evaluation order. All of it encoded as structured reasoning the agent follows on every question. Vetted KPIs so answers reconcile to the penny against her known-correct outputs. Then we built the evaluation harness: golden questions with known correct answers, automated judges, and regression checks against the actual database. The harness existed before the agent did. Six weeks from the PE introduction, the head of product was running real analytical questions through a working agent on live claims data. > $200-$300 a month in compute > Read-only agent inside the client's cloud boundary > Every answer goes through human validation On the day it went live, the co-founder ran a question that normally took four hours. Answer in under a minute. Every reasoning step traced to the source tables. Of the ten hours a week she'd been spending on queries, nine went back to running the business. Compare that to a typical enterprise AI pilot: six to twelve months, $500K or more in consulting fees, and a slide deck as the deliverable.