99% AI Agents Shouldn't Exist. Do This Instead (+ reliable, faster, cheaper)! скачать в хорошем качестве

99% AI Agents Shouldn't Exist. Do This Instead (+ reliable, faster, cheaper)!

As AI agents are going viral, businesses that want to push AI automations to production should really challenge every potential agent they could build to understand if they couldn't actually break it down into more logical steps to gain better control over what happens, limit the cost of the automation and gain in velocity. If the model that your AI agent uses is a very large model (powerful, but slow and expensive), you'll be losing a lot of time and being very inefficient whenever this agent has to think before deciding to call one of the tools, even though it might be extremely easy to figure out that this tool could be called. One of the ways to prevent that is to add an AI routing that uses a very small model to figure out the main use cases and better handle each of them individually instead of having one mega agent that has all the capacities. You should also know that for each step of the agent's process, your whole system prompt will be counted as input token and it will repeat that and add up with also the results of each of the tools, which is highly inefficient. I'm demonstrating in this video a live use case of how you can challenge an AI agent to convert it into an AI workflow that's more reliable and efficient. Important links: Getting started with n8n: https://n8n.partnerlinks.io/55fqdg7pse6y AI agent n8n template I use in the beginning (RAG with Lookio): https://n8n.partnerlinks.io/ai-agent-... My LinkedIn if you'd like to get in touch:   / guillaume-duvernay   CHAPTERS: 00:00 - Why AI Workflows Beat AI Agents 00:59 - The Initial Setup: A RAG AI Agent in n8n 02:15 - Problem 1: Wasting Large Models on Simple Chat 03:51 - Solution 1: Routing Intent with a Text Classifier 09:24 - Problem 2: The Agent's Inefficient Tool-Calling 11:01 - Solution 2: Deconstructing the Agent into a Workflow 15:33 - Final Demo: Testing the Optimized Workflow 16:41 - Conclusion: More Reliable, Cheaper & Faster