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Autonomous agents have emerged as one of the standout innovations in generative AI this year. These tools have demonstrated how AI can be used to generate, prioritize, and complete tasks on the Internet without human supervision.
With an autonomous agent, a user can enter an initial objective or task into a large language model (LLM), and the solution will proceed to complete the task and create follow-up tasks as part of a continuous loop.
This gives these assistants the ability to independently conduct tasks like creating content, writing code, completing research and data analysis, generating to-do lists, creating websites, or even managing a social media account.
In short, autonomous AI Agents provide enterprises with a vast range of use cases they can tap into. Below we’re going to look at 5 of the top autonomous AI agents on the market.
5 Best Autonomous AI Agents
1. AutoGPT
AutoGPT is an open-source autonomous AI agent developed by Toran Bruce Richards, which was released in March 2023. It is designed to be able to interface with GPT-4 and GPT 3.5.
Users can enter a goal or task in natural language, and AutoGPT will proceed to break it down into subtasks before launching new agents to complete those tasks. This could include creating a website, producing social media content, emails, and marketing copy, or even translating text.
It’s important to note that you need to not only install AutoGPT and Docker locally in order to use it but also have an OpenAI account with the ability to create API keys.
2. BabyAGI
BabyAGI is a Python script created by Yohei Nakajima and released in April 2023. It which can automatically create, execute, and prioritize subtasks in real-time using OpenAI’s GPT-4, Pinecone vector search, and the LangChain framework.
The solution functions through the use of three main agents:
- A task execution agent completes the initial task on a task list;
- A task creation agent creates subtasks based on a predefined objective and the result of the previous task;
- A prioritization agent determines the order to complete tasks.
In this framework, GPT-4 generates, completes, and prioritizes tasks; Pinecone stores and retrieves task-related data like descriptions and results, and LangChain enables the agent to be more data-aware and complete tasks/make decisions more effectively.
3. AgentGPT
AgentGPT is an open-source tool developed by Asim Shresta, Srijan Subedi, and Adam Watkins, released in July 2023. It enables users to build and deploy autonomous AI agents via their web browser. Agents can then create and execute sub-tasks.
The program provides a platform for users to build AI agents, where they can assign a name and objective to each and connect to GPT-4 and GPT-3.5 Turbo. AgentGPT can also retrieve task results to learn from them, developing a task execution history stored in the open-source vector database, Weaviate.
Although AgentGPT is similar to other autonomous agents like AutoGPT, the key difference is that it runs as a web-based platform rather than locally. Users can customize the model via the OpenAI API key.
4. SuperAGI
SuperAGI is an open-source autonomous AI agent framework designed to enable developers to build and manage autonomous agents. With SuperAGI, agents can be assigned custom goals and instructions alongside certain tools and open-source models to use.
One notable feature of SuperAGI is that it enables developers to run and manage multiple agents at once via an action console. In the action console, users can enter inputs and configure permissions. There is also an agent feed page where you can view activities performed by an agent in real-time.
It’s worth noting that you need to obtain an API key from an LLM provider, install Docker, and have a GitHub account to use SuperAGI.
5. MicroGPT
MicroGPT is a pre-trained language model developed by Sin Liang Lee, which is trained on an RTX4060 8GB with a 6GB dataset and has 82 million parameters. MicroGPT’s lightweight architecture is designed to use GPT-3.5 and GPT-4 to perform basic tasks.
This includes analyzing stock prices, conducting network security tests or penetration testing, creating digital artwork, or even ordering pizza. The results of each task can be shared to the user’s desktop.
The low number of parameters means that MicroGPT is less suited to larger, more complex tasks. In addition, users must have Python, Git, and a code editor in order to install the program.
The Bottom Line
We’ve just scratched the surface of the fast-growing ecosystem of autonomous AI agents, and there undoubtedly will be more open-source projects emerging all the time as we move into 2024.
That being said, organizations looking to experiment with autonomous AI agents should always assess the risk before adoption to make sure that they aren’t introducing new vulnerabilities that threat actors can exploit.
