https://decodingml.substack.com/p/stop-building-ai-agents
Paul: Today, the scene is owned by Hugo, a brilliant mind who advises and teaches teams building LLM-powered systems, including engineers from Netflix, Meta, and the U.S. Air Force.
He runs a course on the LLM software development lifecycle, focusing on everything from retrieval and evaluation to agent design, and all the intermediate steps in between.
Enough talking, I’ll let him dig into today’s controversial topic: “Stop building AI agents”. ↓🎙️
P.S. I agree with him. 🤫
Hugo: I've taught and advised dozens of teams building LLM-powered systems. There's a common pattern I keep seeing, and honestly, it's frustrating.
Everyone reaches for agents first. They set up memory systems. They add routing logic. They create tool definitions and character backstories. It feels powerful and it feels like progress.
Until everything breaks. And when things go wrong (which they always do), nobody can figure out why.
Was it the agent forgetting its task? Is the wrong tool getting selected? Too many moving parts to debug? Is the whole system fundamentally brittle?
I learned this the hard way. Six months ago, I built a "research crew" with CrewAI: three agents, five tools, perfect coordination on paper. But in practice? The researcher ignored the web scraper, the summarizer forgot to use the citation tool And the coordinator gave up entirely when processing longer documents. It was a beautiful plan falling apart in spectacular ways.
This flowchart came from one of my lessons after debugging countless broken agent systems. Notice that tiny box at the end? That's how rarely you actually need agents. Yet everyone starts there.
This post is about what I learned from those failures, including how to avoid them entirely.
The patterns I'll walk through are inspired by Anthropic's Building Effective Agents post. But these aren't theory. This is real code, real failures, and real decisions I've made while teaching these systems. Every example here comes from actual projects I've built or debugged.
You'll discover why agents aren't the answer (most of the time). And more importantly, you'll learn what to build instead.
What You'll Learn: