The Rise and Rise of AI Agents
The AI agent wave that began with AutoGPT and AgentGPT has now become one of the most interesting facets of modern AI innovation. These so-called agents use LLMs to autonomously complete tasks, with many calling them the precursors to AGI. Moreover, with the introduction of function calling in OpenAI’s APIs, deploying models with agentic behaviour is now easier than ever. Bringing together the power of GPT-4 with custom-written automation programs has created a whole new crop of AI agents, including some that might change the way we interact with the web. With the recent development in the ecosystem surrounding AI agents, such as the function calling update, as well as improvements to LangChain, these agents are becoming more and more powerful. Now, new agents can go beyond creating hustle strategies — they can generate entire codebases and write multi-chapter novels in a matter of minutes. With the rise in accompanying infrastructure and plumbing, we are seeing a new crop of super-powerful AI agents. Building on a legacy Simply put, AI agents use the natural language processing capabilities of LLMs to autonomously accomplish a given task. Seeing the potential of AI agents, market leaders like OpenAI and Hugging Face slowly began building the ecosystem around them. For example, LangChain, which enabled an early form of agentic behaviour by connecting LLMs with external data, has now fleshed out its function calling features. With this update, LangChain has not only added support for OpenAI’s function calling, but also the ability to convert LangChain tools to functions. This enables a wide variety of use-cases, opening up the ability to combine LLMs with even more programs. One of the most remarkable new AI agents is GPT Engineer, a collection of AI systems that can build an entire system from scratch. According to its GitHub page, it can generate an entire codebase based on a prompt and some clarification. What’s more, the agent will even remember the feedback given by the human over time. The repo has around 24.4k stars at the time of writing. Similarly, programmer Matt Shumer created an agent called GPT-Author. This collection of AI models can write an entire book in just minutes, and even generate a fitting cover art and publish to Kindle. While this has already raised concerns, the product has been open-sourced, allowing it to be plugged into even more systems if need be. Shumer stated, “GPT-author is a constrained agent — meaning its behaviour is highly-controlled, leading to better results than open-ended agents.” This could be the key to creating AI agents that actually fulfil a goal faster and more efficiently than a human can. AI agents are often criticised for being inherently iterative. This means that they cannot create something creatively new like humans can. However, constrained AI agents seem to be able to reach human-like levels while reducing errors and staying on topic. The past and future of AI agents Mustafa Suleyman, the co-founder of DeepMind, has stated in interviews that narrow-minded AIs are better suited for constrained use-cases. Speaking about his company’s new AI product, Pi, he stated, “Since we don’t do things like code generation…poems, legal letters, and song sheets, we can basically free up a lot of the language model to focus just on general knowledge, trivia, and a conversational style.” The AI agent trend arguably began with AutoGPT, launched in March this year. The GitHub repo quickly garnered over 100,000 stars, receiving rave reviews from the likes of Andrej Karpathy. Hailed as the “next frontier in prompt engineering”, AI agents began to grow in popularity. Developers used AutoGPT to automate data scraping, to write and self-debug code, and even find ways to hustle on the Internet to make some extra money. The success of AutoGPT resulted in a bunch of other AI agents, such as AgentGPT, AutoGPT, Do Anything Machine, and more. A look at LangChain’s Twitter feed shows how much functionality this update has added. Developers have created bots that can pull from GitHub repos to answer questions, a service to create websites from a scratch, and even a smart database for farmers. When combined with the updated and cheaper OpenAI API, AI agents have created a buzz in the developer community. It now seems that developers are more eager to play with LLMs, resulting in some interesting agents being released. This might be the path to AI agents that we can actually use. Similar to Tim Berners-Lee’s prediction that everyone will each have their own AI assistants in the future, a collection of constrained AI agents might become the way we interact with the web.
Growth in the AI ecosystem has given rise to a new crop of AI agents — a trend that doesn’t seem to be going anywhere
Growth in the AI ecosystem has given rise to a new crop of AI agents — a trend that doesn’t seem to be going anywhere
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["Go", "Hugging Face", "AI assistants", "OpenAI", "AI", "Git", "LangChain", "prompt engineering", "R", "AutoGPT"]
["AI", "OpenAI", "LangChain", "AutoGPT", "Hugging Face", "prompt engineering", "AI assistants", "R", "Go", "Git"]
I am an AI enthusiast and love keeping up with the latest events in the space. I love video games and pizza.