On GitHub, the star count for AutoGPT has exceeded 100,000. This represents a new type of human-computer interaction: you do not need to tell the AI what to do first or next; instead, you simply set a goal for it, even something as simple as “create the best ice cream in the world.” Similar projects include BabyAGI and others. What does this wave of autonomous agents mean? How do they operate? What will they look like in the future? How can one experiment with this new technology? In this article, Octane AI’s CEO and co-founder Matt Schlicht provides a detailed introduction.
Artificial intelligence can be used to accomplish very specific tasks, such as recommending content, writing copy, answering questions, and even generating photos indistinguishable from real life. You tell the AI which task to complete, and it does so, very simply.
However, what if you don’t want to help the AI list out all these tasks? What if you want a teammate rather than just a tool? What if you want the AI to think autonomously?
Imagine you create an AI tool that can set goals, even vague ones like “create the best ice cream in the world,” and it will draft a to-do list, execute the tasks, and add new tasks based on progress. It will continue this process until the goal is achieved.
This is precisely what “autonomous agents” do. In the AI developer community, they are growing at the fastest rate, but so far, most people do not understand them. (At the time of writing this article, no mainstream publication has covered autonomous agents, and since their inception, very few have reported on them. Therefore, if you are reading this article… you are one of the first to learn about it.)
What are autonomous agents? What huge opportunities lie behind them? How do they operate? What will they look like in the future? How can I create or use them?
These are the questions I will answer for you.
[Intelligent] Autonomous agents are typically the automated endpoint of processes. In principle, agents can be used to automate any other process. It is not hard to imagine that once these agents become highly sophisticated and reliable, the level of automation across various fields and industries will grow exponentially.” — Bojan Tunguz, Machine Learning Expert at NVIDIA
What Are Autonomous Agents?
Autonomous agents are AI-powered programs. Given a goal, they can create tasks on their own, complete tasks, create new tasks, re-prioritize their task list, complete new priorities, and continuously repeat this process until the goal is achieved.
Please read the above description again; while it is simple, it is also quite astounding.
From the trend of autonomous agents, everyone is expected to become a manager.” — Yohei Nakajima, Founder of BabyAGI
Autonomous agents can be designed to do anything, from managing social media accounts and investing in markets to creating the best children’s books.
“Are these real? Can they be achieved now?”
Yes, I know this sounds like science fiction, but they do exist. If you can code, you can design one in minutes. And this is just the beginning.
People often waste too much time on tedious manual work, and when computers can handle these tasks, people can be freed to pursue more creative endeavors or do things that only humans can do. Autonomous agents will enable people to accomplish more in less time, and over time, the time people spend staring at screens is expected to decrease!” — Erica Brescia, Managing Director at Redpoint
The programming skills and AI needed to achieve autonomous agents are very realistic and extremely novel. Many open-source projects, such as AutoGPT, BabyAGI, and Microsoft’s Jarvis, are popular in the AI community and on GitHub.
In the first two weeks of creating the open-source autonomous agent codebase, nearly 100,000 developers were building, optimizing, and exploring the limits of these agents, and this work was done only in the first few weeks after these concepts were invented. Today, the number of developers using this technology is skyrocketing at an increasingly rapid pace.
AI agents will be everywhere. Companies worth billions will emerge from small teams deploying AI agents.” — Ben Tossell, Founder of Ben’s Bites AI Newsletter
The growth scale of AI agents has already surpassed long-popular codebases such as Laravel, Bitcoin, Django, and PyTorch.
The popularity of Auto-GPT on GitHub is growing exponentially, faster than any codebase in history.
This is not science fiction. Many believe that these autonomous agents are the true beginning of general artificial intelligence, also known as “AGI” — a term used to describe AI that has gained consciousness and become “alive.”
“Autonomous agents may ultimately commodify all applications of factual knowledge. If access to factual knowledge becomes universally available, then human qualities such as creativity, emotion, and strategic vision will become more precious and unique. However, knowledge may also become increasingly proprietary as individuals and companies seek economic advantages in a world where the commodification of factual knowledge and the stagnation of collective human knowledge begin to occur.” — Tony Hu, Former Deputy Chief of Emerging Technologies at the FBI, Co-Founder of Bondoo AI
Take a look at this autonomous agent just released by HyperWrite; you can see it installed in a browser, helping people order pizza.
You simply say, “Order a cheese pizza with no toppings from Dominos to One Vanderbilt,” and it can order it by itself.
HyperWrite’s autonomous agent controls the browser to order pizza.
Or, take a look at this experiment completed in collaboration between Stanford University and Google, where they created a virtual town with 25 autonomous agents and told one of them to organize a Valentine’s Day party. This example may be even more impressive.
These autonomous agents simulate people living their lives, talking to each other, forming new memories, and ultimately most of them heard about the Valentine’s Day party and attended.
Image Source: “Generative Agents: Interactive Simulacra of Human Behavior”
So autonomous agents are real… This raises the question: as long as you tell the agent what the goal is, will it manage itself forever?
The answer is yes.
You just need to set a goal for it, and the rest will be handled by the autonomous agent itself. It is like a very good employee or teammate. However, if you wish, you can also design the autonomous agent to contact you at key decision moments so that you can guide its work in real-time.
This is primitive AGI. It is worth noting that simply wrapping an LLM in a loop can yield an autonomous agent that can reason, plan, think, remember, and learn on its own. If the wrapping and prompts are correct, the LLM can be triggered to unleash endless potential and flexibility. Although the entire concept has only been around for less than a month, I can’t wait to see how complex agents built with LLMs will impact the world.” — Chen Siqi, Founder and CEO of Runway
In addition to analyzing goals and assigning tasks, these autonomous agents also possess a range of capabilities, such as:
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Browsing the internet and using applications;
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Long-term and short-term memory;
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Controlling your computer;
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Using large language models (LLMs) like GPT for analysis, summarization, providing opinions, and answering questions.
Moreover, these autonomous agents will come in various shapes and sizes. Some will operate in the background without users knowing what they are doing, while others will be visible, as shown in the previous example, allowing users to track every “thought” of the AI.
Autonomous agents will allow everyone to live like a head of state! You just need to make requests, and the autonomous agent will handle the rest. You won’t need to waste mental energy on routine or mundane tasks.” — Chris Yeh, Co-Author of “Blitzscaling”
Next, let’s illustrate with a straightforward example: suppose there is an autonomous agent that can assist with research; we want a summary of the latest news on a certain topic, say, news about Twitter:
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We tell the agent, “Your goal is to find the latest news about Twitter and then send me a summary”;
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Thus, upon seeing the goal task, the agent, aided by AI like OpenAI’s GPT-4, understands the content it is reading and proposes its first task: “Task: search Google for news related to Twitter”;
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Then the agent searches for Twitter news on Google, finds popular articles, and returns a list of links. The first task is completed;
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Next, the agent reviews its main goal (to find the latest news about Twitter and send a summary) and what it has just completed (getting a bunch of news links about Twitter) and decides what its next task should be;
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After that, it proposes two new tasks: 1) write a news summary. 2) read the news links found through Google;
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Now the agent pauses before continuing its work; it needs to ensure the order of these tasks is correct. Should it write the summary first? The agent denies this; it decides that the priority task is to read the content of the news links found through Google first;
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The agent reads the content from the articles, then returns to its to-do list. It wants to add a new task to summarize the content, but since that task is already on the to-do list, the autonomous agent does not add it;
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The agent checks the to-do list, and the only remaining task is to summarize what it has read, so it does so. At this point, it sends the summary as requested by the user.
The chart below illustrates how autonomous agents work:
Image Source: Yohei Nakajima’s BabyAGI
This new paradigm has only just begun, but it is not perfect and has not yet become popular worldwide, but the concept is very powerful, and as it continues to develop and experiment, it will soon integrate into our daily lives.
This will soon change many industries. By using autonomous agents, people can do many things more easily at the same time. Just give it a task, and it will get it done. So far, this is a very powerful concept…” — Barsee, Founder of The AI Valley Newsletter
Having understood to a greater extent what autonomous agents are, let’s discuss why they can bring such great opportunities.
Let’s delve deeper.
If we can obtain the information we need faster, will that free up time for us to focus on thinking and doing things? Since this AI agent can execute more tasks, can people spend less time on tedious work, leading to better and more creative ideas?” — Marina Pérez, Customer Management Director at Octane AI
Why Autonomous Agents Can Bring Such Great Opportunities
It is clear that soon you will not only be able to choose to hire humans as employees but also hire AI in the form of autonomous agents.
“Before long, I believe you will see a significant increase in the number of startups combining tools like AutoGPT and ChatGPT with 1 to 2 people. They will be able to achieve the kind of progress you previously expected from a 100-person startup. In the long run, I believe most jobs can and will be replaced by AutoGPT.” — Nathan Lands, Founder of Lore
Moreover, they will not be as expensive as hiring humans; they do not sleep, do not resign, and work very efficiently.
One of my points when I founded Product Hunt in 2013 was to believe that the barriers to building software products would continue to lower, enabling smaller teams (or individuals) to create software faster than ever before. Driven by AI and autonomous agents, this is more true today than ever. This brings anxiety to some and opportunities to others, who leverage this technology to scale their ideas with fewer people and funds. Ultimately, consumers will benefit from the intense competition between businesses and the constant emergence of new solutions.” — Ryan Hoover, Founder of Weekend Fund and ProductHunt
These autonomous agents will exist in every industry and can be applied to every imaginable task. The following image shows just a few examples:
This list can continue to grow. What humans can do, autonomous agents will (eventually, but soon, and in some cases already) be able to do better.
The music industry imposes too many unnecessary tasks between artists and success. These tasks cost artists nearly 35% of their net income. Autonomous agents will be able to devise and execute marketing strategies, interact with fans, build communities, book venues, and negotiate contracts, saving money and time for artists.” — Troy Carter, Co-Founder of Venice Music, Former Manager of Lady Gaga
How can we seize the opportunity? There are two very realistic opportunities.
Create autonomous agents yourself and make them available for others to hire;
Hire autonomous agents that can now assist in improving your quality of life or business efficiency.
Autonomous agents are the next wave — not just in technology but across the entire business landscape. I predict that within 10 years, there will be multiple billion-dollar companies entirely run by autonomous agents. This is inevitable.” — Ben Parr, Co-Founder and President of Octane AI
Imagine a world where one person builds a company with only autonomous agents on their team. In your lifetime, you will likely see an individual team capable of achieving a valuation of over $1 billion, which typically requires many people working together to accomplish.
Mass personalization will become a very interesting use case. You will be able to control multi-step processes that humans execute today, including generating personalized images, videos, websites, and even emails, or even large-scale calls. One use case that could attract significant interest is sales outreach.” — Omar Pera, Head of AI Products at Meta
Now, in the early stages, pioneers, whether creating or using autonomous agents, will gain a significant advantage over competitors who have not yet utilized these systems.
In the near future, I hope to see lunch meetings, calls, and interviews appear on my calendar without my involvement in arranging them. My agent and their agent can do that, handling all the details. I just need to show up.” — Hugh Howey, Bestselling Author of “WOOL”
By reading this article, you are already ahead of 99% of the world. Let’s delve deeper into more details about how these autonomous agents work.
Autonomous agents have the potential to enhance the output of smaller content creators and community members, especially those with creative imaginations. This will be a boon for many Web3 projects. (“Web3.0” is a derivative of the underlying protocol of the current internet, “the World Wide Web.” It means machines can read any information, websites can provide intelligent filtering based on information, and provide better information (AI), the internet is everywhere (IoT), and more importantly, the ownership of internet data will be decentralized.)” — Jeffrey Zirlin, Co-Founder of Axie Infinity
How Autonomous Agents Work
You have a general understanding of how autonomous agents work, but I think it would be helpful to provide you with a holistic framework version and break down several examples of autonomous agents step by step.
I now view AI as a whole, and we are in the stage of evolving it into an artificial intelligence assistant, similar to what we see in movies — like Jarvis in “Iron Man” or TARS in “Interstellar.”
Now is the time to build the framework. Because AI itself is still improving, the answers it provides may not be perfect and may contain errors. But looking back at how much AI has progressed over the past six months, I think we can hardly imagine the advancements in AI in the next 1-2 years. So it’s about experimenting early and quickly, preparing for the future.” — Jenny Reece, Consumer Insights at Microsoft
Here is a general framework for an autonomous agent:
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Initialize goals: Define the AI’s objectives;
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Task creation: The AI checks its memory for the most recently completed X tasks (if any), then uses its goals and the environment of recently completed tasks to generate a new task list;
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Task execution: The AI autonomously executes tasks;
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Memory storage: Tasks and execution results are stored in a vector database;
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Feedback collection: The AI collects feedback on completed tasks in the form of external data or internal dialogues. This feedback will be used to inform the next iteration of the adaptive process loop;
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New task generation: The AI generates new tasks based on the collected feedback and internal dialogues;
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Task prioritization: The AI re-prioritizes the task list by reviewing its goals and the last completed tasks;
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Task selection: The AI selects the top task from the prioritized list and continues executing them as described in step 3;
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Iteration: The AI repeats steps 4 to 8 in a continuous loop, allowing the system to adjust based on new information, feedback, and changing needs.
Gabriel Menezes, Engineering Director at Octane AI, states: “Autonomous agents really fascinate me because they embody the properties of the ultimate productivity booster. As someone who highly values the automation of monotonous or repetitive tasks, I find these agents have the potential to completely change the way we work, allowing us to direct our mental energy toward more meaningful pursuits.”
Example Showcases
Example 1: Social Media Manager Autonomous Agent
Suppose you do not want to hire a social media manager to manage your social media accounts but rather hope that an autonomous agent can do everything for you with minimal cost and intelligence around the clock.
This is not just a virtual assistant. This is a revolution that accelerates all online work, research, and even entertainment. Tasks that previously took hours, days, or months to complete online can now be done in minutes in the background.” — Sharon Zhou, Stanford University CS Faculty and Former Machine Learning Product Manager at Google
Here is what the framework of an autonomous agent might look like:
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Initialize goals: Set initial parameters such as target audience, social media platforms, content categories, and posting frequency;
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Data collection: Gather data on past social media posts, user interactions, and platform-specific trends. This may include likes, shares, comments, and other engagement metrics;
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Content analysis: Analyze the collected data to identify patterns, trending topics, hashtags, and influencers relevant to the target audience. This step may involve natural language processing and machine learning techniques to understand the content and its context;
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Content creation: Based on the analysis, generate content ideas and create social media posts tailored to the platform and audience preferences. This may involve using AI to generate text, images, or videos, and incorporating user-generated content or curated content from other sources;
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Scheduling: Determine the best times to post each piece of content based on platform-specific trends, audience activity, and desired frequency. Schedule posts accordingly;
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Performance monitoring: Track the performance of each post based on engagement metrics (such as likes, shares, comments, and click-through rates). If possible, gather user feedback to further refine understanding of audience preferences;
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Iteration and improvement: Analyze performance data and user feedback to identify areas for improvement. Update content strategies, creation, and scheduling processes to incorporate these insights. Repeat steps 2-7 to continually refine the social media management system and improve its effectiveness over time.
People will have personal agents to communicate with other agents owned by others and businesses. Most computing devices will primarily serve as communication devices to interact with agents.” — Conner Ruhl, Senior Software Engineer at Stability AI
By incorporating this cyclical system into social media management, you can create a dynamic adaptive strategy that evolves with audience preferences and the ever-changing social media landscape. This will help improve engagement, influence, and overall effectiveness of social media work.
Another use case of autonomous agents that excites me is their application in music creation. By leveraging the capabilities of AI-driven algorithms, these agents can analyze my personal preferences, favorite genres, and even specific musical elements that resonate with me. They can then generate original melodies, harmonies, and rhythms, effectively co-creating music with me. This creative collaboration has the potential to broaden my musical horizons and allow me to explore new styles and genres that I may not have considered before. Additionally, autonomous agents can provide valuable feedback on my work and offer suggestions for improvement, helping me grow as a musician. The fusion of AI and human creativity in the music creation process can yield innovative and unique results, expanding the boundaries of artistic expression.” — Katya Sapozhnina, Product Director at Octane AI
Example 2: Autonomous Agent Capable of Running for Office
If you are running for a position and want to leverage an AI assistant for help.
I hope the agent does not have to do difficult work, but these tasks require some time and effort. For example, things like booking flights, I am happy to outsource to the agent.” — Sahil Lavingia, Founder and CEO of Gumroad
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Initial goal: Ensure winning the election by securing the majority of votes;
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Data collection: Gather data on voters, demographics, key issues, campaign information, and other relevant data;
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Background analysis: Analyze the collected data to identify trends, opportunities, and challenges. Refine the initial goal into specific sub-goals based on this analysis, such as targeting undecided voters, increasing voter turnout in key areas, or improving campaign messaging on specific issues;
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Task generation: Generate tasks related to the refined sub-goals, such as planning voter outreach events, creating targeted ads, or developing policy proposals;
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Task execution: Execute the highest-priority tasks, allocating resources and assigning team members as needed;
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Performance monitoring: Assess the effectiveness of completed tasks by tracking key performance indicators such as voter engagement, public opinion, and fundraising metrics. Evaluate the success of individual tasks and the overall campaign’s progress in achieving sub-goals and the initial goal;
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Iteration and improvement: Analyze performance data to identify areas for improvement and update campaign strategies based on these insights. Repeat steps 2-8 to continually refine the campaign management system and improve its effectiveness over time.
I am very excited about the recursive self-cloning capability. AI agents can create their own copies, pass task instructions, and start communicating with their siblings to complete work. This is a remarkable yet strange emergent capability.” — Jim Fan, AI Scientist at NVIDIA
Initially, one candidate may use one autonomous agent, giving them a significant D advantage over others, but imagine what it would be like once every candidate has one… or multiple agents.
I don’t think everyone will use autonomous agents. While they will be ubiquitous, as AI evolves, human involvement in work will revive. Many will rethink the pen and paper, wanting art made by humans… We will see many products and creations touting “entirely made by humans.” It should soon become a very popular label. The faster technology advances, the more I will enjoy long periods of complete offline time, and soon I will also enjoy “time away from AI.”” — Loic Le Meur, Founder and CEO of PAWA | Loic
Example 3: Autonomous Agent for Teaching Math
There will also be autonomous agents designed to teach children math.
This is a groundbreaking paradigm with a lot of exploration space. Although early experiments limited the agent’s search queries, we will see a wide range of research and assistance projects equipping autonomous agents with new tools. And each set of tools will significantly expand their potential use cases.” — Pete Huang, Founder of The Neuron Daily AI Newsletter
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Initial goal: Determine the current math skill level of the child and set a personalized learning path to help them improve;
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Data collection: Gather information about the child’s learning styles, processes, and performance through assessments, interactions, and feedback;
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Context analysis: Analyze the collected data to identify the child’s strengths, weaknesses, learning preferences, and all external factors affecting the child’s progress;
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Task generation: Generate tutoring tasks based on the child’s needs and learning path, such as selecting appropriate exercises, providing explanations, or offering real-life examples and applications;
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Task prioritization: Prioritize tutoring tasks based on their potential impact on the child’s learning and skill development, finding a balance between challenge and engagement;
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Task execution: Execute the highest-priority tasks, adjusting tutoring methods and content delivery as needed to maximize the child’s learning mastery and engagement;
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Performance monitoring: Evaluate the effectiveness of tutoring by tracking key performance indicators (KPIs), such as progress on learning goals, improvement in math skills, and the child’s engagement and satisfaction;
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Feedback loop: Continuously monitor the child’s performance and update context analysis, task generation, and task prioritization steps based on new data and insights. Adjust the initial goals and learning paths as needed to better support the child’s math skill development;
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Iteration and improvement: Analyze the child’s performance and update context analysis, task generation, and task prioritization steps based on new data and insights. Adjust the initial goals and learning paths as needed to better support the child’s math skill development. Repeat steps 2-9 to continually refine the educational management system and improve its effectiveness over time;
This cyclical system of autonomous agents outlines the process of adaptive assistance and guidance for children’s learning experiences in mathematics, focusing on continuously improving and providing personalized guidance based on the child’s needs and progress.
The Future of Autonomous Agents
Humans are currently in the early stages of developing autonomous agents. We are exploring, breaking some things, experimenting, and creating things that are either good or bad.
By requesting the help of autonomous agents, they will turn your ideas into reality. These agents can serve as friends, colleagues, and collaborators, providing you with ample leisure time. I wonder how you would choose to spend this newfound free time?” — Kazuki Nakayashiki, Co-Founder and CEO of Glasp
Currently, there are almost no commercial autonomous agent products released; this type of product is still in the development stage. But soon, this situation will change. Autonomous agents will start appearing everywhere.
Rather than focusing on replacing people’s jobs, we should focus on enhancing their capabilities. Making something “smart” in the past meant making its data available via API. The next generation of intelligence will be about asking how the product can better assist you. For example, a “smart” email address might be able to act in interesting ways based on your preferences. If you are a shopping enthusiast, perhaps it will monitor emails to know when the products you are interested in go on sale, compare prices, or even negotiate prices on your behalf, privately understanding how much you value the product and how much you are willing to pay for it.” — Matt, Managing Partner at Factorial Capital, Investor at HuggingFace
People will enhance their activities, decisions, and actions through various autonomous agents. If at some point in the future we have neural implants, all of this will happen naturally, just like thinking in your own mind today.
Everyone will have access to virtual researchers, assistants, writers, or staff for free or at a low cost. This access will be democratized.” — Jeremiah Owyang, AI Investor
Here are my predictions for the future of autonomous agents:
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In 2023, multiple commercial autonomous agents for gaming, personal use, marketing, and sales will emerge;
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In 2024, various categories of commercial autonomous agents will emerge, but without mainstream adoption;
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In 2025, autonomous agents will be commonly adopted in every conceivable category;
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In 2026, most people in developed countries will live their daily lives with the assistance of numerous autonomous agents;
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In the next 2-5 years, most people will work for autonomous agents rather than humans.
I see a Holodeck powered almost entirely by AI using augmented reality, where many things happen automatically and with manual prompts. Yes, people will work for AI. Everyone will use them, but only a few will know what they are or how to create them. With the continuous emergence of large language models (LLMs) and upcoming autonomous agents and systems, the world is about to undergo profound changes.
LLMs are the most democratizing force invented by humans. Why? LLMs can now run on inexpensive computers without needing to connect to a central server. That little engine essentially contains all human knowledge. Incredibly, you can run it on devices that are not connected to the internet. Autonomous agents just make this Holodeck operate almost automatically. From weather to pizza delivery, everything happens automatically, with minimal human input required.” — Robert Scoble, Chief Strategy Officer at Infinite Retina AI-First
The future will be crazy. So how do we build and use autonomous agents?
In this future, everyone may use autonomous agents in some capacity, whether for personal productivity, business operations, or creative activities. In most cases, people will act as “masters” for these AI agents, setting goals for them and pushing them forward. We will also “work for AI agents,” just as we must work within the constraints of companies, processes, and other systems. However, I believe that AI agents will often outperform the companies and systems present in today’s society and create opportunities that benefit everyone.” — Joe Heitzeberg, Co-Founder of Crowd Cow
How to Build and Use Autonomous Agents
You are now ready to dive into the world of autonomous agents. I will list the resources you need to start building or using autonomous agent proxies.
Find a specific B2B use case with a lot of repetitive tasks. Such as sales operations, advertising operations, project operations, accounting services, etc. There are many tasks available now.” — Elizabeth Yin, Co-Founder of Hustlefund
First, narrow down your use case as much as possible. Then, design a product that includes a human-in-the-loop (HITL) and a method for assessing whether the process is successful, gradually increasing the level of automation, and finally expanding to adjacent use cases.” — Itamar Friedman, Co-Founder and CEO of Codium AI
Building Autonomous Agents
There are several different options for building autonomous agents.
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Build it yourself: Take a look at the framework I provided earlier and embark on the journey of building everything from scratch! It’s not as scary as it sounds. Recommended software solutions include OpenAI’s GPT-4, Pinecone vector database, and the LangChain framework.
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Auto-GPT: This is a popular open-source option created by Toran Richards. It includes options for connecting to the internet, using applications, long-term and short-term memory, etc.
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BabyAGI: Another popular open-source option created by Yohei Nakajima. While this one is not yet connected to the internet, its code is less than 200 lines, making it very concise.
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Microsoft’s Jarvis: Very similar to Auto-GPT and BabyAGI, but more powerful, provided by Microsoft and HuggingFace.
I believe we will initially have vertical autonomous agents. These agents are fine-tuned for a specific set of data, enabling them to operate effectively in that domain. So far, we have seen a lot of applications of large language models (LLMs) in only two fields: copywriting and programming. Further inference suggests that it makes sense for AI adopted in these two fields to become more autonomous. One way this could gradually emerge in the near future is that AI will replace humans in providing prompts to trigger copywriting or code writing, automatically suggesting new ideas for you to consider each day without needing you to initiate or prompt them first.” — Lonis Hamaili, Creator of godmode.space
Using Autonomous Agents
Ready to have your own agent? Here are some options.
You can create your own agent as described above by choosing any of the options!
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AgentGPT: Create and run autonomous agents (AutoGPT) from the website without logging in.
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HyperWrite Assistant: Add a Chrome extension that allows you to issue commands to the browser, which then executes them.
People from all walks of life can benefit from professional knowledge and efficient methods that were previously reserved for the social elite. This democratization of personal assistance can lead to higher productivity and a more balanced work-life experience, enabling people to focus more on their interests, creativity, and personal growth, while their AI assistants take care of the more monotonous parts of their daily lives.” — Matt Shumer, Founder and CEO of HyperWrite
Whether or not you can code, I encourage you to spend a few hours trying these things out. It’s not as complicated or difficult as it seems, and the sooner you get started, the quicker you will understand autonomous agents.
As an investor, using autonomous agents to complete the work of analysts and assistants, or at least greatly assist them, excites me. They can programmatically search for trades under specific conditions, analyze based on specific factors, and then send me custom emails to start conversations.” — Brayton Williams, Co-Founder of Boost VC
Autonomous agents can now be open to interpretation and innovation. 99% of use cases have yet to be created or attempted, with endless possibilities, and the opportunities are in your hands.
I am very interested in orchestrating and modularizing smaller programming tasks for achieving larger ultimate goals. We know that large language models excel at programming based on questions, but we have yet to see evidence that they can port entire codebases from Android to iOS or even create applications from scratch. I believe that agents with the right orchestration and memory structures could achieve this.” — Neal Khosla, Co-Founder and CEO of Curai
Original link: https://www.mattprd.com/p/the-complete-beginners-guide-to-autonomous-agents?continueFlag=d7ee95bb4d852ef440fb8296d95c54e6
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