From Hype to Hard Reality: What We're Learning About Shipping AI Agents in Production
Originally published on tamiz.pro . For the better part of two years, the narrative has been seductive: build a few function calls around a reasoning model, add a tool-use loop, and you have an autonomous agent that solves hard problems. The demos are real. The benchmarks are impressive. And then you try to ship one to production — and the floor opens up beneath you. I've spent the last eighteen…
The hype surrounding AI agents has been strong for the past two years, with many believing that by simply adding function calls and tool-use loops to a reasoning model, they could create autonomous agents capable of solving complex problems. However, once these agents are deployed in production, the reality falls short of the hype.
The author, who has spent 18 months working on AI agent systems across various teams and products, argues that there is a significant gap between the promises of AI agents and their actual performance at scale. This gap is explored in the article, which serves as a guide for engineers navigating this gap.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
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