GENERATIVE AI

A client asked us to build a multi-agent system for their marketing chatbot. They had the whole thing mapped out. One agent for planning. One for retrieval. One for generation. One for validation. A full squad.😅 I'll be honest, it looked good on their pitch deck. Probably helped get the grant, too. But something felt off when we started asking questions. "Does agent 2 ever run without knowing what agent 1 decided?" No. Always sequential. "Does the validation step ever happen without the full generation context?" No. Every step depends on the one before it. "Does the flow ever change mid-execution?" Almost never. So we told them: you don't need four agents. You need one agent that's good at using tools. That's a conversation most clients don't love having. They came in wanting the architecture. We came back saying the architecture is the problem. We built one agent with 8 tools instead. One decision maker holding the full context. Tools doing the specialized work: formatting SMS, validating tone, retrieving brand assets. No information dropped at handoffs. No duplicated logic. Way easier to debug. And it's cheaper, faster, and more reliable. I keep seeing this pattern. We reach for multi-agent because it feels like the serious choice. But splitting context across agents when the task is sequential just creates problems you didn't have before. 🤷‍♂️ One agent with good tools is still the most underrated architecture out there. Before your next build, try the recipe test: if you can write the exact steps in advance like a recipe, it's a workflow or one agent. Not a multi-agent system. [Translated from EN to English]

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