Since the launch of OpenAI's GPT-4 API last month to beta testers, a loose group of developers has been experimenting with making agent-like ("agentic") implementations of the AI model that attempt to carry out multistep tasks with as little human intervention as possible. These homebrew scripts can loop, iterate, and spin-off new instances of an AI model as needed. Two experimental open source projects, in particular, have captured much attention on social media, especially among those who hype AI projects relentlessly: Auto-GPT, created by Toran Bruce Richards, and BabyAGI, created by Yohei Nakajima. They need a lot of human input and hand-holding along the way, so they're not yet as autonomous as promised. But they represent early steps toward more complex chaining AI models that could potentially be more capable than a single AI model working alone.

In recent months, we have seen the emergence and proliferation of several artificial intelligence systems worldwide, such as OpenAI's ChatGPT, GPT-4, and Google's Bard. Microsoft's new Bing and Baidu's Ernie Bot have also entered the scene. Joining this group of AI systems is a newcomer known as BabyAGI. BabyAGI is an innovative AI platform designed to train and evaluate various AI agents in a simulated environment. The AI is a pared-down version of the original Task-Driven Autonomous Agent developed and launched by VC and AI expert Yohei Nakajima.