Find a Real Job for Your First AI Agent

YouTube/Podcast/Substack · ~21 min · YouTube · Podcast · Read

• Case study: closed 51 of 52 weekly support tickets with an agent, then discovered 39 of the 52 were the same root cause (a recurring Slack-access problem) — the team had been “repairing one broken way” by hand, dozens of times.
• Named framework: the three-payoff model for agent deployment — the customer gets a faster answer, the team stops doing repetitive resolution work, and the root-cause pattern feeds back into product fixes.
• Methodology: sort support tickets by root cause rather than subject line/category to surface the single most-repeated problem; a time study found that reconstructing customer data across systems (not writing the reply) is the expensive part of support work.
• Gumroad example on agent-autonomy boundaries: their support agent found a charting bug, wrote the test, and shipped the fix autonomously — but got a design decision wrong in a way only the customer could catch, illustrating where agents should stop and hand off to human/customer judgment.
• Practical takeaway: start your first agent deployment on your last 50 support tickets, categorised by root cause — support is “the best place to start with agents” because it gives fast, measurable feedback on success or failure.