The Agent Prototype Trap: 15 Things That Break When You Hit Production
Last Updated on August 28, 2025 by Editorial Team
Author(s): Mayank Bohra
Originally published on Towards AI.
Turns out, getting AI to answer questions is the easy part. Shipping something reliable is where the real fun begins.
It almost always starts innocently enough. You’re messing around with an LLM API, feed it some context, maybe a prompt, and bam — it gives you something useful back. You add a tool, chain a couple calls together, and before you know it, you think you’ve built an agent. It works perfectly in your notebook or demo script. You’re ready to conquer the world.
This article discusses the challenges of transitioning from an LLM prototype to a reliable production system, outlining key principles such as state management, structured knowledge, and the importance of abstraction and modularity. It emphasizes the necessity of building with engineering discipline, integrating human oversight, and managing errors contextually to create steadfast AI agents that can perform in real-world settings.
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