We've all seen the flashy headlines and endless LinkedIn posts promising that AI can do everything. You’ve probably tried to build an AI workflow yourself, only to be left with a vague, unhelpful result. We’ve been there, and we can tell you the most likely reason your AI project failed: you're asking it to do too much.
(You can see the full chat we had in the video).
The biggest mistake people make is thinking AI is "hyper-intelligent" and can handle a massive, complex task with a single prompt. We think we can connect "a billion tools to one single AI agent" and it will magically figure everything out. We see a LinkedIn post listing 20 different AI tools and a comment that says soon there will be "a one single agent that can do all of those tasks". This belief is a fundamental misunderstanding of how AI, especially large language models (LLMs), actually works.
Why the "Do-It-All" AI Fails
Even Google isn't building a single, all-encompassing AI agent. They have Gemini integrated into everything from Google Workspace to their search engine, but it's not one massive entity. Instead, it's a collection of "small agents that are doing specific tasks". If a company with Google's "brain power and... capital" isn't doing it, why do we think we can?.
The problem with trying to give an AI a 10-step process in one prompt is that it gets confused. You're creating a "black box" where you can't see what's happening inside. You can't tell if a specific step "hallucinated" and if that initial error is going to "builds on itself until you get something that's entirely fictional and made up". Earlier this year, Iqbal built four AI agents and deleted every single one because it was impossible to trust the output. Even with tools designed to make data analysis easier, you never truly know how the AI is figuring things out.
The Unreliability of the First Answer
LLMs are not fact-based machines; they are prediction engines. They are built on predicting relationships, and a core part of their power is their "variance", the fact that they don’t always give the same answer. This is great for creative tasks, but it's a huge problem for business and other important applications.
Sani recently did a test with an LLM, asking it the same vague prompt twice to optimize a webpage. In one chat, the output recommended SEO as a priority; in the other, it didn't. How can you trust the first output an AI gives you when it can't even be consistent with itself? You can't afford to have your business depend on a system that can be wildly different from one minute to the next. As the chat at the bottom of these services says, "Gemini can make mistakes. Always verify your output".
The Solution: Be the Orchestrator
To get useful results, you must take control and become the orchestrator of your AI workflow. Instead of giving a single agent a huge task, break it down into the smallest possible steps. Then, give each of those small steps to a separate, specific AI agent.
This approach allows you to correct the output at each stage. You can correct the output if it doesn't fit what you expect it to be, and then pass it on to the next node. This is how you prevent a small error in the first step from growing exponentially and ruining the entire project. By keeping a human in the loop, you can catch it and you can correct it.
AI is an incredible tool, but it's not a replacement for human reasoning and oversight. The most effective and reliable AI workflows are those that are specific, modular, and have a human making sure every step is on track. Stop trying to make a magic box and start building an assembly line.
Keep building!
Iqbal & Sani
