Let the model interpret. Let software validate. Let workflows own state, approvals, and side effects.
Most AI app demos make the same mistake:
they treat the model like the application.
Prompt in, answer out, maybe a few tool calls, then everybody acts surprised when the thing becomes weird in production.
The problem is not that the model is useless.
The problem is that we keep asking a probabilistic component to behave like deterministic workflow code.
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Yann LeCun argues LLMs will drive real-world applications, but not human-level thinking
The new AI lock-in
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Executive Briefing: Stop asking if AI can do this. Start asking what shape the work is.
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LLM Evaluation and AI Observability for Agent Monitoring | The PyCharm Blog
Further Notes on Our Recent Research on AI Delegation and Long-Horizon Reliability - Microsoft Research
