"The pressure is on. Every conference, every tech blog, every corner of the internet is buzzing with AI agents, autonomous workflows, and the promise of a revolution powered by large language models (LLMs). As a developer, it's easy to feel like you need to integrate AI into every feature and deploy agents for every task. But what if the smartest move isn't to use AI, but to know when not to?"
"This isn't a contrarian take for the sake of it; it's a call for a return to engineering pragmatism. The current hype cycle often encourages us to reach for the most complex, exciting tool in the box, even when a simple screwdriver would do the job better. Just as you wouldn't spin up a Kubernetes cluster to host a static landing page, you shouldn't use a powerful, probabilistic LLM for a task that is, and should be, deterministic."
AI hype pushes developers to apply LLMs and agents everywhere, creating incentives to overuse powerful tools for simple tasks. Using LLMs for deterministic operations introduces cost, unpredictability, and complexity penalties compared with traditional code. Engineers should apply pragmatism: use straightforward, deterministic solutions where appropriate and deploy LLMs only when tasks require probabilistic reasoning, contextual understanding, or automation beyond simple logic. Avoid over-engineering by matching tools to task requirements, optimize for robustness and efficiency, and use a decision framework to evaluate when AI and agents deliver meaningful benefits relative to their costs and risks.
Read at LogRocket Blog
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