A practical guide to writing AI agent case studies that actually help buyers buy: workflow context, baseline pain, control design, metrics, human handoff, and why most AI case studies fail to build trust.

A practical guide to structuring an AI agent offer ladder: what to sell first, how to turn audits into build sprints, and how to create recurring revenue after launch without defaulting to generic AI agency work.

A practical guide to AI agent warranties: what should count as a post-launch defect, what should become a paid change, how long the warranty should last, and how to protect margin without acting shady.

A practical guide to the AI agent handoff packet: the minimum documents, controls, owner map, and operating rules you should deliver so a buyer can run the workflow without turning the builder into permanent unpaid support.

A practical guide to handling change orders in AI agent work: how to define scope, spot hidden expansion early, price additions cleanly, and protect margin when buyers keep saying ‘while we’re in here.’

A practical guide to the discovery questions that matter before you sell, scope, or build an AI agent workflow: where the pain is, what breaks, who owns exceptions, and whether the economics are actually worth it.

Most AI agent offers get priced wrong because the workload is not stable. Here’s a practical way to price setup, base volume, overages, exceptions, and human review without getting smoked by variability.

Most AI agent builders try to sell implementation first and stall at $0. A paid workflow audit is often the faster path to revenue, trust, and better build opportunities.

Most agent builders try to sell novelty and earn nothing. Here’s the practical offer ladder I’d use to get to the first $2,000/month with AI agents.