If artificial intelligence is creating new technology at breakneck speed, it is also creating a new vocabulary just as quickly. Terms like “prompt engineering” entered the mainstream a couple of years back. Now, newer phrases such as “loop engineering” and “forward deployed engineer” are beginning to appear across job listings, startup pitches and industry conversations.
Are these terms reflecting genuine shifts in how AI systems are being built and deployed? The short answer, according to people inside the industry, is yes.
Take Anthropic PBC co-founder Boris Cherny, who created the coding tool Claude Code. Cherny had previously proclaimed that “software engineering is dead.” Now he asserts that the era of manual AI prompting is drawing to a close. That’s a striking statement from someone whose company builds one of the most widely used AI assistants on the planet.
So what does it mean, in plain terms? Think of it like the difference between giving someone a single instruction and teaching them a whole recipe.
A prompt engineer might write a prompt asking an AI to summarise a legal document. A loop engineer, by contrast, would build a system that reads the document, identifies ambiguities, searches relevant regulations, reviews its own conclusions and flags uncertain cases for human review. As AI becomes more agentic — meaning it can take multiple steps on its own — the challenge shifts from writing better prompts to designing better processes.
That makes loop engineering a natural evolution of the role. Here’s the practical upshot: loop engineering is about encoding expertise. And the best loop engineers are often not the people who know the most about AI.
They are the people who understand a domain — such as media, law, medicine, finance, or software development — and can translate that expertise into a repeatable sequence of checks, decisions and actions. These stages coexist, by the way.
A coding agent still relies on good prompts. A research assistant still benefits from careful context management. Loop engineering simply becomes more important as you move from simple one-shot tasks to complex, multi-step workflows.
What this means for ordinary readers is that the job market is shifting under our feet. The person who can describe a legal process in enough detail to teach an AI to follow it may soon be more valuable than the person who can write the perfect one-line prompt.
It’s a move from asking the machine the right question to teaching the machine the right procedure. Industry watchers expect these roles to proliferate. The term “forward deployed engineer” is already appearing in job listings, suggesting companies want people who can go out into the field — into real businesses with real processes — and build these looping systems on the ground.
The bottom line: if the first wave of AI jobs was about talking to the machine, the next wave is about teaching it how to think in steps. That’s a very different skill set, and it rewards deep domain knowledge over technical prompt-crafting. For anyone wondering what comes next in the AI job market, following the vocabulary might be the smartest move.
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