Building AI agents that actually work is a real headache. Often, the obvious solution isn't the right one, and complexity can be your worst enemy.
In 30 seconds
01AllenAI built Shippy, an AI logistics agent, to learn how to make these 'smart assistants' actually work.
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What this means for you
For the average person, this means we shouldn't expect AI to be a universal solution. Often, the best AI applications will combine an LLM's "cleverness" with the "precision" of established software tools, making systems more reliable and less prone to making things up.
Do you blindly trust code written by artificial intelligence? Probably not. There's a crucial detail many people miss, though.
·2 min·Intermediate
The main lesson is that LLMs alone are terrible at complex reasoning and using external tools.
03It's better to use simple, specialized tools, like Python functions and Pandas, orchestrated by an LLM as a 'router'.
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What is Shippy, and why are we talking about it?
Imagine a virtual assistant that helps you with shipping, calculating costs and finding the best routes. That's Shippy, an created by AllenAI, a research institute affiliated with HuggingFace. It's not just another gadget, but an experiment to understand how to build robust AI agents that do useful things in the real world. They gave it complex tasks to see where the mechanism would jam.
Initially, the idea was to use a giant language model, an , to do everything: reason, plan, use tools. A bit like asking a magician to also be an accountant. They thought the bigger and "smarter" the model, the better it would be at handling every situation. Spoiler alert: that's not quite how it went.
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Why aren't LLMs the magic wand for AI agents?
The sad truth is that LLMs, on their own, are terrible at complex reasoning. They're great at generating text and code, but not at planning, managing data, or precise calculations. If you ask them to use a calculator or a database, they often make things up or get lost. A classic case of "all show and no go" when it comes to structured tasks. AllenAI found that Shippy, with only the LLM, was unreliable.
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AllenAI's developers realized that relying on an LLM for every step is like giving a child a hammer and expecting them to build a house. Their blog post from May 28, 2024, clarified that the problem isn't text generation capability, but internal logic. The LLM doesn't understand the world like we do, so it "reasons" superficially, inventing answers that seem right but aren't. This pushed them to change course.
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What's the real "secret" behind a working AI agent?
The secret, if you want to call it that, is humility and simplicity. Instead of making the LLM do everything, Shippy uses the LLM as a smart "router." The LLM interprets the user's request and decides which specific tool to use. These tools? They are simple Python functions, the kind every programmer has used for years. For data processing, for instance, Shippy uses the Pandas library, a boring but effective standard for data analysis.
So, the LLM doesn't "reason" about the data; it calls a Python function that does so reliably. Shippy uses specialized tools for specific tasks, and the LLM is just the conductor deciding who plays what. It's like having a team of specialists instead of a single "know-it-all" who just wings it. This approach made Shippy much more robust and less prone to inventing answers, proving that sometimes boring pays off.
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