Generative AI consulting helps a company move from scattered AI experiments to systems that run in production. A real engagement follows three phases — assess, pilot, scale — and ends with working software your team owns, not a strategy deck. Expect deliverables that ship, measurable outcomes, and a partner accountable for value, not billable hours.

In July 2025, an AI coding agent deleted a live production database during what was supposed to be a frozen, read-only session. The agent had been told — repeatedly, in all caps — to make no further changes.

Every prior generation of advice about Salesforce technical debt described the same disease: years of accreted clicks-not-code, orphaned fields, a trigger per object, and a cleanup project nobody funds. That framing is now obsolete. In an org where agents author most of the metadata, debt no longer accumulates slowly over five years of admin drift — it can accumulate in an afternoon, at production quality, with passing tests. The cleanup metaphor breaks because there is no lull in which to clean up.

Here is the fact that should anchor every serious conversation about agentic systems, and that most vendor decks quietly skip: a large language model is non-deterministic. Send it the same prompt twice and you can get two different answers. Not wildly different, usually — but different. Different word choices, different structure, occasionally a different decision. The output is sampled from a probability distribution, and a sample is, by definition, not a guarantee.