It’s easy to assume that a stronger model will fix wrong answers. But what if the model was never the problem?

AI coding agents can now turn a short prompt into a working feature in minutes. Whether it is the right feature, built the right way, is a different question.

Elements.cloud gives Salesforce teams deep insight into their orgs: metadata, dependencies, change impact, tech debt, compliance. Reaching that insight wasn't always easy, and adoption showed it.


An agent can recommend a loan, flag a patient, or reject a job candidate in milliseconds. Getting that speed is straightforward. The hard part comes later, when a rejected applicant asks why, an auditor wants the reasoning, or a regulator asks who signed off. That is the black box trap: a system that produces answers no one on your team can trace or defend.

Healthcare is drowning in data. The industry now generates nearly 36% of the world's data, producing 10,800 exabytes of information every year.

Agentic coding can build a working prototype in an afternoon. Whether it can build a product you'd trust in production, alone, without a team, is a different question. That gap is what this article is about.

The global AI market was estimated at $371.71 billion in 2025 and is projected to reach $2,407.02 billion by 2032, growing at a 30.6% CAGR, according to MarketsandMarkets. That scale has attracted a large and diverse field of AI development companies, which makes choosing the right partner harder.

Gartner expects over 40% of agentic AI projects to be canceled by the end of 2027. Across the healthcare industry, the gap is just as stark, with only 21% of organizations reporting mature governance for agentic AI, even as 74% expect to use AI agents by 2027. Production lags far behind.

Your clinicians didn't go to medical school to fight with insurance forms. Yet that's where a huge share of their day goes.

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