How to scope an AI agent project without wasting budget

Start with a job, not a mood

“We should use AI” is not a requirement. A useful agent answers a repeatable, bounded question in a system you already have: a queue to triage, a corpus to query, a draft to first-pass, a threshold to check before a human is paid to care.

Where scoping usually breaks

Teams over-index on the model and under-specify: the workflow (handoffs and exceptions), data authority (what the agent is allowed to believe), and the failure budget (what a wrong answer costs in time, money, and trust). Fix those before you size compute.

A sane pilot

Time-box, measure, and be willing to stop or narrow when the work is really process or data hygiene in disguise. That is a feature, not an admission of defeat: it is cheaper to fix a broken handoff in your CRM than to “prompt engineer” your way out of a missing business rule.

What Barberry can do next

If the shape of your case looks real, a short AI agent scoping engagement or a paid architecture session is usually a faster path to a quote and delivery plan than another generic demo.

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