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There is an uncomfortable truth hiding inside every enterprise AI initiative: the moment you make production cheap, you make AI model validation expensive. Not in licensing fees or compute costs — in the hardest currency of all: organizational certainty that what your agents are producing is actually correct.

This is the validation avalanche. And if your engineering org isn't ready for it, the collapse happens quietly, buried under a growing pile of outputs no one has the capacity to check.

For a large share of healthcare organizations, Salesforce Health Cloud has quietly become the system of record for everything that is not the EHR: care coordination, patient and member outreach, intake, eligibility, prior authorization, referrals. It is the connective tissue between clinical systems and the humans who run operations around them. And in 2026, two separate AI questions are landing on the same desk at the same time.

Every agentic demo you have ever been impressed by was greenfield. A clean repository, a well-scoped problem, a fresh prompt, and an audience watching live. The agent writes the feature, the tests pass, the room nods. It is genuinely impressive, and it is genuinely easy. A capable model on an empty canvas with a human steering in real time is close to a solved problem.

That is not the problem you have.

Rework rarely announces itself. It shows up disguised as productivity — a revert here, a "fix:" follow-up there, a branch quietly closed and re-cut on a new base. Each looks like a small course correction. In aggregate, they are the single largest tax on a delivery stream, and in an agentic one — where an AI author ships fast and there is no default human reviewer standing between the model and the merge button — they compound faster than anyone expects.