An Autonomous AI Agent

??Introduction

Intelligence is not confined to only knowledge. However, it is conditioned by Knowledge for a wide range of activities. We decide what to do based on what we know (or believe) about the environment, effortlessly and unconsciously.

As we all know, the cost of language models (LLMs) is decreasing annually, approaching a point where they will essentially be cost-free. Moreover, they are becoming increasingly accessible and user-friendly, enabling practically anyone with rudimentary programming skills to begin using them. Therefore, the invention of automation with the help of LLMs is inevitable.

In this blog, I will attempt to explain the concept of an autonomous agent and elucidate why I am describing it as digital labor.

??Transition

We have come across various interfaces to interact with computers. Initially, there was the command line interface, followed by the graphical user interface, and now we have AI-assisted interfaces. Looking ahead, the future could resemble AI assistants like Tony Stark's J.A.R.V.I.S. (Just A Rather Very Intelligent System) or F.R.I.D.A.Y. (Female Replacement Intelligent Digital Assistant Youth). ??

??Autonomous Agent (aka Language Agents)

Over the past few months, many studies have delved into language agents utilizing LLMs for sequential decision-making. While LLMs still encounter obstacles when dealing with intricate reasoning, a significant amount of research underscores their capability to formulate plans.

Autonomous AI Agents represent intelligent entities with the ability to make decisions and carry out actions independently, without or with less human involvement. These agents rely on sophisticated algorithms and machine learning models to analyze data, derive insights, and perform tasks autonomously.

Therefore, autonomous AI agents can excel in complex situations, even when faced with limited information, by leveraging reasoning with LLMs. They can swiftly adapt to new circumstances, generate innovative ideas and solutions, and engage in more natural interactions with humans.

Agents function as digital workers with the versatility to use a variety of tools interchangeably. For example, in modern times, agents can perform tasks such as conducting browser searches, executing computer programs, and acting as Software Engineering (SWE) assistants. Specifically, they can debug and modify code, analyze Git issues, generate code based on given scenarios, serve as data analysts, train machine learning models, and perform various other functions. Additionally, autonomous agents have the potential to design, execute, and enhance marketing strategies, conduct R&D experiments, etc.

?Examples

Below are a few well-known examples. While there are many more, I have chosen only a few for illustrative purposes.

???BabyAGI - https://github.com/yoheinakajima/babyagi

?? AutoGPT - https://github.com/Significant-Gravitas/AutoGPT

???JARVIS/HuggingGPT - https://github.com/microsoft/JARVIS

?? MetaGPT - https://github.com/geekan/MetaGPT

???Devin- https://devinai.ai/

???Devika - https://github.com/stitionai/devika

???AgentLite - https://github.com/SalesforceAIResearch/AgentLite

???OpenInterpreter - https://www.openinterpreter.com/01

??The Framework

Numerous frameworks exist for developing autonomous agents, and it mainly depends on the goal that we want to accomplish. Primarily, to optimize the effectiveness of autonomous agents in achieving their objectives, the process involves three key stages: action selection, LLMs selection, and tool selection.

1-?????? Action: Action selection is a crucial phase comprising several sub-steps that guide an agent through problem-solving. This stage involves synthesizing a team of agents and formulating an execution plan tailored to the task at hand by analyzing the input. Subsequently, the execution stage fine-tunes the plan through inter-agent collaboration and feedback, culminating in the outcome. Inter-agent collaboration is governed by defined rules of cooperation, including communication, coordination, and consensus. These principles facilitate information sharing among agents, alignment of actions, agreement reaching, and environmental adaptation.

2-?????? LLMs: The selection of LLMs heavily influences the capabilities of all agents. Choosing the appropriate LLMs depends on the problem type; for instance, when developing an autonomous agent for software engineering (SWE), selecting LLMs trained on a code database is essential.

3-?????? Tools: Tools serve as interfaces enabling agents to interact with the environment. These interfaces comprise a set of predefined functions/methods that accept specific arguments and execute lines of code. Input for these tools can be obtained from LLMs through JSON parsing or function calls. Examples of tools include calculators, Python REPL (Read-Eval-Print Loop), CSV data analyzers, search engines, and more.

Conclusion

Agents will be everywhere and work as digital labor across various business sectors. The development of human-like behaviors in these agents, aligning with principles from social psychology hypotheses, underscores their considerable prospect.? Therefore, despite the uncertainties, businesses must prioritize information security and ethical AI practices. Additionally, we need to establish mechanisms to ensure that the decisions made by these autonomous agents agree with organizational values and comply with legal norms. It necessitates addressing fairness, explainability, security, and bias.

Thank you for taking the time to read!??