That depends on your definition of "Agent": the term has been warped by AI hypesters from the original ReACT paper to the point of being meaningless because it

That depends on your definition of "Agent": the term has been warped by AI hypesters from the original ReACT paper to the point of being meaningless because it sounds cool. The more notable common paradigm of Agent workflows that will persist even if there's an AI crash is retrieval-augmented generation (RAG), which at a high-level essentially is few-shot text generation based on prior existing examples. There will always be value in aligning LLM output to be much more expected, such as "generate text in the style of these examples" or "use these examples to answer the user's question." Startups that just market themselves as "chat with your data!", even though they are RAG based, are gimmicks though and won't survive because they have no moat.