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Autonomous AI Agents: Bridging the Gap to AGI

Autonomous AI agents are a significant leap forward in the development of AI. Looking at their capabilities, they surpass chatbots – agents can, in fact, learn and make decisions.

Dorota Jasińska

Content Specialist

Andrzej Puczyk

Head of Delivery

With the rise and rapid development of AI, we can see its increasing use and capabilities. Now, we can observe a hint that the creation of AGI, artificial general intelligence, is getting closer. AGI can learn and perform a specific task independently from a user. IT still remains a theory, but autonomous AI agents may be considered a turn towards making it a reality.

What are autonomous AI agents?

Autonomous AI agents are one of the newest innovations in the field of GenAI. They’re considered a new solution that can strongly influence businesses. Autonomous AI agents are complex AI systems that can perform tasks independently. They can learn and adapt to their environment and make decisions to meet a specific goal.

Some people consider them the next level of copilots that are more capable than the GenAI tools and platforms that we use and know now. Why is that? Mostly because autonomous AI agents will be able to understand what their goal is and learn how to achieve it. They can redesign workflow to do what’s required from them and automate it. All that without the need for human supervision.

In short, an autonomous AI agent can prioritize and complete a task without any help. Their workflow is based on iterating their actions until they reach their goal.

Matt Schlicht described an example of how such agents could work, for example, by helping out in research on the news about Twitter. Then, he analyzed step by step how it would reach the objective. First, AI divides the objective into tasks that need to be completed (research, filtering information, making a summary). Then, it completes a task and reevaluates what else has to be done and in what order. Once it figured out the proper order, it performs the tasks while continuously checking their relevancy. So, the agent knows it can’t produce a summary of the research without finding the right sources first and then filtering the useful information. It verifies if, in the process, no new tasks appear that may influence the objective.

How do autonomous AI agents work?

Autonomous AI agents are advanced systems that use LLM to make decisions. However, to do so, they need to be equipped with tools and memory.

Tools enable LLM to get real-time data from the web, databases, and other sources.

Memory makes it possible for the agent to look into the past and see what has already been done. This way, the agent can learn from past actions and find patterns that are relevant to its objective.

These two, combined with LLM, create an agent that has more capabilities than bots and tools we know. When an agent has knowledge from tools, access to memory, and language models, it can use them to make decisions. The agent uses reinforcement learning to find feedback from its actions by trial and error. This way, the agent knows what to do next.

Here’s a diagram of the process created by Yohei Nakajima, the creator of BabyAGI:

In BabyAGI, the process works as follows:

  1. The user has to provide the agent with an objective. An objective is then divided into tasks, and this creates a task queue.
  2. The execution agent completes the tasks and sends task results to the task creation agent. The tasks are also stored in the memory.
  3. Task creation agent uses the memory to find context for tasks and adds more tasks and subtasks to the task queue.
  4. The task prioritization agent sets up the correct order of tasks. If tasks have already been completed, the prioritization agent removes them from the list.

The decision-making process, in this case, uses knowledge base, memory, and LLM. The agent analyzes the data, searches the memory for relevant information, and chooses the best options to complete the objective.

How are autonomous AI agents different from chatbots?

Autonomous AI agents have broader capabilities compared to chatbots. The main difference is that chatbots work based on prompts, and their job is to fulfill one task using the input instructions. And this is when their job ends. Autonomous AI agents perform a more complex mission. They’re focused on completing an objective that includes multiple tasks that need to be ordered and evaluated before the objective completion.

Another difference is that a chatbot doesn’t learn from its responses. It neither stores what it produces nor knows if it is good or bad. Autonomous AI agents can learn and store the feedbacked information in the memory to refer to it and find the best solution. With feedback, they can improve and complete their objectives well.

Chatbots generate text responses based on the user input. Autonomous AI agents go a few steps further and can execute complex actions and operations using tools and memory. They can make a task list, prioritize, and order them to finally complete the objective.

Are autonomous AI agents the beginning of AGI?

The reference to AGI appears in many articles about autonomous AI agents. They are indeed more capable of performing more complex tasks than AI chatbots. Yet, AGI is still just a concept, and autonomous AI agents may be a possible step towards it. They’re not capable of what AGI should be able to do, like learning to perform actions like a human. They can, however, remember and learn, but still, they don’t match the AGI’s skills. Autonomous AI Agents use LLM, memory, and tools and can perform limited actions. They can manage tasks, browse the web, manage a PC, etc., but this doesn’t mean they can perform on a human-like level, which is basically what AGI is.
Top autonomous AI agents


AutoGPT is an open-source AI agent created by Toran Bruce Richards. It is publicly available on GitHub. The autonomous AI agent works with OpenAI’s API for GPT-4. The agent has the ability to divide an objective into tasks without a user’s help. Then, it arranges the tasks in the order that will help complete the objective. It also has access to the web, so it can use up-to-date information to perform tasks. AutoGPT also offers a ready-to-go template to create your own agent, along with tutorials.


BabyAgi is an AI task management system that uses OpenAI and Pinecone APIs. It can create, prioritize, and execute tasks. This is a Python script created by Yohei Nakajima that uses three agents to fulfill an objective: execution agent, task creation agent, and prioritization agent. Once the objective is defined, the agent breaks it down into a few tasks. Then, it takes one task from the list and sends it to the execution agent that completes the task. The result is stored in a vector database. This allows for creating a new task and setting up a new order for the existing tasks. The execution agent, task creation agent, and prioritization agent then complete their jobs in a loop.

Agent GPT

AgentGPT was created by Adam Watkins, Asim Shrestha, and Srijan Subedi. AgentGPT makes it possible to configure and deploy autonomous AI agents on a website. The user can choose an agent that already exists on the site or create a new one. AgentGPT also allows using external tools, such as Google search or code review. The user can also modify the agent model via the OpenAI API key. The fact that this tool is available through a website makes it easy to use for non-technical users.


SuperAGI is an open-source framework for developers to build and manage autonomous AI agents. It allows the creation and running of custom agent workflows using LLM ReAct architecture. Developers can augment the agents with custom knowledge by plugging in embeddings from the marketplace or using their own ones. It also enables extending agents with tools such as GitHub, Jira, Slack, and Notion or adding a self-created toolkit.


MetaGPT is a multi-agent framework that offers different agent roles for a user. It was created by a group of researchers from Chinese and US universities. This is an LLM-based programming framework that allows collaboration in multi-agent systems. The main purpose is to assign different roles to GPT that will make a collaborative agent for complex objectives. MetaGPT includes product managers, architects, project managers, and engineers acting as agents who can provide the entire process of a software company.


AutoGen was created by Microsoft in cooperation with Pann State University and the University of Washington. AutoGen enables the building of complex multi-agent conversation systems. It’s a conversation framework with an open-source library. Users can create their own LLM workflows, maximizing the performance of LLM models. Users can use multiple agents capable of learning independently and collaborating on what the user requests. AutoGen includes agents that support diverse conversation patterns and are customizable.

How can autonomous AI agent be used?

Autonomous AI agents seem like a game-changer for many processes. Their ability to perform a task and learn thanks to access to memory definitely suggests they could be implemented in various fields. However, we should remember their applicability is still limited. There are risks associated with reliability, malicious use, and the threat of cyberattacks.

Some of the AI agents from the list above are already used and implemented by companies to facilitate various processes. They can be used for tasks like project management, business management, customer support, finance, or document automation. These are just a few examples of their implementation, as workflow automation, task execution, and other options for user assistance are still being developed and improved.

Let’s take a closer look at MetaGPT, which specializes in providing multi-AI role collaboration with agents who are bringing best practices for the user. MetaGPT offers adherence to standardized operating procedures (SOPs), so the code you get is vetted. The agents can be used to perform specific tasks with the use of GPT-3.4 architecture and other AI systems. It can produce different outputs, like competitive analysis, documents, data structures, and more. It’s also capable of implementing human collaboration techniques to improve its outputs.

MetaGPT is a tool that can be used in software development, app and content creation, and more.

Neontri’s agent of choice: Professor Synapse

Neontri has very specific needs and uses AI’s capabilities to facilitate work. That’s why we implemented Professor Synapse to help us achieve our goals. Professor Synapse was created by Synaptic Labs and can be integrated into ChatGPT+. Professor Synapse works as a conductor of expert agents and can be adjusted to user needs. It learns a domain and knows contexts.

As an expert, it’s capable of understanding the task and dividing it into steps that are verified by the user. Once it learns the task, it gathers context, relevant information and asks for a specific goal.

Overall, the use of Professor Synapse reminds a conversation with a specialist who asks additional questions that are helpful to complete the task.

To present how it works, we asked Professor Synapse to help us write a social media post about this article:

Here, Professor Synapse acts as an expert in content creation and social media. It understands its role and gives us an idea of how it processes the information to achieve the goal. Let’s look at the process it completed to achieve our objective:

These are just a few steps in the process, but what’s important is that the last one involves asking for feedback to provide the best possible result. The process goes in a loop until the user is satisfied with the outcome.

The future of autonomous AI agents

Looking at the speed of AI development, we may expect autonomous agents to gain popularity and be implemented into everyday life. Now, they are tools used mostly by technically advanced users and companies. However, according to this publication, autonomous agents will go mainstream within three to five years.

Such agents can streamline customer service or perform simple tasks, like ordering a pizza, so the potential of their rising popularity in common use is more than likely. Even now, self-driving cars using autonomous AI agents capable of making decisions are already present and may gain popularity in the future.

As autonomous AI agents may give us a promise of further innovation in the world of tech, we still need to wait to see how they will develop. They may bring revolution in business operations, decision-making processes, and work efficiency. Their continuous advancement is definitely worth observing because, at some point, they may be able to integrate into our lives and work.

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