I've built AI agents for dozens of clients. Here's why most of them fail in production (and it's not the model)

I see a lot of people shipping AI agents that work perfectly in s and fall apart the moment a real user touches them. After building automation systems for multiple clients, I've noticed the failures almost never come from choosing the wrong LLM. They come from three things: 1. Bad chunking in RAG pipelines. Everyone's so focused on picking the right vector DB that they don't think about how they're splitting documents. Garbage in, garbage out. If your chunks don't preserve context across sentences, your retrieval will always be mediocre. 2. Prompts written for s, not edge cases.