While building Brayniac, I wanted to share some challenges and solutions that might generally apply to building AI agents using large language models

Productionalizing AI agents

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2023-06-03 15:30:07

While building Brayniac, I wanted to share some challenges and solutions that might generally apply to building AI agents using large language models (LLMs). The challenge lies in how to harness the power of LLMs to create effective and safe AI agents that act as part of an application.

An AI agent is a computer program that takes user prompts and performs actions based on them. These actions can range from data analysis to internet research, and even tasks like making restaurant reservations or booking flights. The possibilities are endless, but the commonality is that the LLM gets user input and takes actions using common computer tools such as a code interpreter or APIs.

LLMs are incredibly powerful, and AutoGPT has shown what is possible with AI agents. However, while AutoGPT has blown everyone's mind it is also still suffering from many issues. Even simpler agents such as the LangChain Python Agent come with challenges when integrated into an app. Namely:

The first approach involves restricting the LLM to a predefined set of actions using rules or syntax. This has actually been mentioned by @yoheinakajima himself. We can provide the LLM different actions it can take via the prompt and how it can provide input parameters. We then parse the LLM output and extract what action should be taken with which parameters. For example, in data analysis, we can use a predefined markup like "MEAN X". By checking if the output contains "MEAN" and "X," we can compute the mean of the column X. This can also be implemented via LangChain tools.