The real-life version of Westworld is one of the hottest topics these days. Picture a digital world where 25 AI agents exist within a simulated world. They go about their daily routines —working, gossiping, socializing, forming friendships, and even falling in love. Each AI agent has its own distinct personality and backstory, making them as unique as any human being. The place is called Smallville, according to the paper Generative Agents: Interactive Simulacra of Human Behavior.
Stanford and Google researchers worked together to unveil a novel architecture that lets AI bots simulate human behavior. Smallville mimics a typical small town, complete with a cafe, bar, park, school, dorm, houses, and stores. AI agents live in spaces and their behaviours came up with themselves without pre-programming; they were merely given a text prompt by human users.
That’s when AI agents became the center of our talks, again.
Currently, AI agents are a hot topic with broad application prospects in many products and businesses. Many believe that agents will be the future entrance to large language models. Within enterprises, agents can be used in complex task scenarios to help maximize labor productivity.
Defining AI Agents
According to Lilian Weng, former head of applied AI research and current head of safety systems at OpenAI, there are three key characteristics to define an AI agent:
Memory: AI agents combine the ability to use short-term memory to process chat-based prompts and follow-up questions with long-term data retention and recall. This often involves retrieval augmented generation (RAG), allowing them to access and utilize a wider range of information.
Planning: AI agents can generate step-by-step plans with discrete milestone goals from a given prompt. They also learn from their mistakes through a reward system, continuously improving future outputs.
Tool Use: Agents can query APIs to request additional information or execute actions based on an end user’s request, expanding their capabilities and functionality.
AI Agent=LLM + Memory + Planning Skills + Tool Use
Understanding the Functionality
An AI agent is a self-service autonomous tool, performing tasks on behalf of a user. They can perform tasks and respond to situations based on their environment, automating processes, making decisions, and interacting intelligently with their surroundings. Think of it as a self-driving car – it takes in information, processes it, and acts accordingly.
Memory: A Key Difference
While the three defining characteristics are key research directions for AI agents, let’s delve deeper into memory. Language models themselves don’t possess memory in the way humans do. Their memory structure is completely different. Humans have working memory, short-term memory, and long-term memory. Language models only have a rough equivalent of working memory. Short-term and long-term memory are essentially impossible to implement in current language models. This is because they are designed for compression, making it difficult for them to perform incremental tasks beyond compression.
The human brain forms memories through complex mechanisms. Long-term memory formation takes weeks to months, while short-term memory formation takes less time. Both exist in a distributed manner within our brains, with neurons acting as both storage and computation units.
When it comes to implementing memory in current agents, people often consider RAG (Retrieval Augmented Generation). However, RAG is very different from human memory. Human memory has a fundamental reliability guarantee. Once something is forcefully memorized, it’s very difficult to forget.
AI Agents vs. AI Chatbots
Many think AI agents to be chatbots. Imagine a chatbot as a vending machine – limited to dispensing pre-programmed items. Now picture an AI agent as a personal chef. This chef boasts an impressive repertoire of recipes (a vast knowledge base), understands complex dish requests (natural language processing), and can even learn new meals tailored to your preferences (ability to learn from historical data). This analogy highlights the fundamental difference between chatbots and AI agents.
While both are designed for interaction, AI agents possess capabilities that far surpass those of chatbots. Chatbots operate on rule-based dialogues, confined to answering pre-defined questions. Their responses are often scripted and lack the ability to reason or connect to broader knowledge. In contrast, AI agents can reason, grounding their answers in relevant knowledge and content, providing more nuanced and contextually appropriate responses.
Training a chatbot can be a time-consuming process, requiring extensive training on hundreds of utterances to understand natural language requests. AI agents, on the other hand, are significantly quicker and easier to implement. They don’t rely on rule-based dialogues or complex configurations, making them more adaptable and flexible.
Chatbots follow scripted conversation workflows, while AI agents utilize generative AI and natural language processing (NLP) to understand, respond, and take actions on customer queries. In essence, chatbots regurgitate predefined information, while AI agents can reason and provide more insightful responses.
Generative AI unlocks capabilities that surpass the scripted workflow experience of traditional chatbots. As businesses adopt generative AI, customers experience a significant improvement in the quality of interaction.
Onboarding an AI Agent is akin to welcoming a new employee with boundless potential. Unlike traditional chatbots, AI Agents can instantly connect to your existing knowledge base, absorbing information in seconds. Once onboarded, the AI Agent empowers customers by reasoning through the best solution, operating like a human agent. It identifies relevant information, outlines clear steps to resolve the issue, and delivers a personalized solution.
AI agents and chatbots also differ in their purpose. While chatbots are designed to interact with humans, AI agents are designed to complete autonomous tasks. The most significant difference lies in their ability to take independent action. Since AI chatbots are primarily focused on human interaction, they are typically not programmed to act autonomously. Their purpose is to directly assist a human user.
This isn’t a futuristic concept; businesses leading the way in AI are already leveraging this technology today.
The Future of AI Agents
The AI era is just beginning, and its evolution is breathtaking. From the dawn of computers to the internet, from the first large language models to the emergence of advanced agent technology, technology continues to expand our world at an astonishing pace.
This evolution is poised to reshape the business landscape. Interacting with AI assistants is already commonplace in large organizations. As technology advances and agents become more capable of independently completing complex tasks, their scope will expand across industries.
The buzz surrounding AI agents is well-deserved. As they continue to evolve, they will be able to collaborate on increasingly complex tasks, reducing the need for extensive prompt engineering by users. For developers, the benefits are clear: AI agents free them to focus on higher-value activities.
When LLMs are equipped with tools, memory, and the ability to plan, they become akin to LEGO blocks, ready to be assembled into more sophisticated systems. AI agents, at their best, are modular, adaptable, interoperable, and scalable, just like LEGOs. Developers can use them to build multi-agent systems, promising to revolutionize software development.
At Cloudsway, we are excited about the potential of AI agents, agentic AI, and multi-agent systems for software developers. We invite you to build with Cloudsway or host your agents with us. Let’s embark on this journey together.
Blog 5: The Westworld in Real World, When AI Agents Take Center Stage
The real-life version of Westworld is one of the hottest topics these days. Picture a digital world where 25 AI agents exist within a simulated world. They go about their daily routines —working, gossiping, socializing, forming friendships, and even falling in love. Each AI agent has its own distinct personality and backstory, making them as unique as any human being. The place is called Smallville, according to the paper Generative Agents: Interactive Simulacra of Human Behavior.
Stanford and Google researchers worked together to unveil a novel architecture that lets AI bots simulate human behavior. Smallville mimics a typical small town, complete with a cafe, bar, park, school, dorm, houses, and stores. AI agents live in spaces and their behaviours came up with themselves without pre-programming; they were merely given a text prompt by human users.
That’s when AI agents became the center of our talks, again.
Currently, AI agents are a hot topic with broad application prospects in many products and businesses. Many believe that agents will be the future entrance to large language models. Within enterprises, agents can be used in complex task scenarios to help maximize labor productivity.
Defining AI Agents
According to Lilian Weng, former head of applied AI research and current head of safety systems at OpenAI, there are three key characteristics to define an AI agent:
Memory: AI agents combine the ability to use short-term memory to process chat-based prompts and follow-up questions with long-term data retention and recall. This often involves retrieval augmented generation (RAG), allowing them to access and utilize a wider range of information.
Planning: AI agents can generate step-by-step plans with discrete milestone goals from a given prompt. They also learn from their mistakes through a reward system, continuously improving future outputs.
Tool Use: Agents can query APIs to request additional information or execute actions based on an end user’s request, expanding their capabilities and functionality.
AI Agent=LLM + Memory + Planning Skills + Tool Use
Understanding the Functionality
An AI agent is a self-service autonomous tool, performing tasks on behalf of a user. They can perform tasks and respond to situations based on their environment, automating processes, making decisions, and interacting intelligently with their surroundings. Think of it as a self-driving car – it takes in information, processes it, and acts accordingly.
Memory: A Key Difference
While the three defining characteristics are key research directions for AI agents, let’s delve deeper into memory. Language models themselves don’t possess memory in the way humans do. Their memory structure is completely different. Humans have working memory, short-term memory, and long-term memory. Language models only have a rough equivalent of working memory. Short-term and long-term memory are essentially impossible to implement in current language models. This is because they are designed for compression, making it difficult for them to perform incremental tasks beyond compression.
The human brain forms memories through complex mechanisms. Long-term memory formation takes weeks to months, while short-term memory formation takes less time. Both exist in a distributed manner within our brains, with neurons acting as both storage and computation units.
When it comes to implementing memory in current agents, people often consider RAG (Retrieval Augmented Generation). However, RAG is very different from human memory. Human memory has a fundamental reliability guarantee. Once something is forcefully memorized, it’s very difficult to forget.
AI Agents vs. AI Chatbots
Many think AI agents to be chatbots. Imagine a chatbot as a vending machine – limited to dispensing pre-programmed items. Now picture an AI agent as a personal chef. This chef boasts an impressive repertoire of recipes (a vast knowledge base), understands complex dish requests (natural language processing), and can even learn new meals tailored to your preferences (ability to learn from historical data). This analogy highlights the fundamental difference between chatbots and AI agents.
While both are designed for interaction, AI agents possess capabilities that far surpass those of chatbots. Chatbots operate on rule-based dialogues, confined to answering pre-defined questions. Their responses are often scripted and lack the ability to reason or connect to broader knowledge. In contrast, AI agents can reason, grounding their answers in relevant knowledge and content, providing more nuanced and contextually appropriate responses.
Training a chatbot can be a time-consuming process, requiring extensive training on hundreds of utterances to understand natural language requests. AI agents, on the other hand, are significantly quicker and easier to implement. They don’t rely on rule-based dialogues or complex configurations, making them more adaptable and flexible.
Chatbots follow scripted conversation workflows, while AI agents utilize generative AI and natural language processing (NLP) to understand, respond, and take actions on customer queries. In essence, chatbots regurgitate predefined information, while AI agents can reason and provide more insightful responses.
Generative AI unlocks capabilities that surpass the scripted workflow experience of traditional chatbots. As businesses adopt generative AI, customers experience a significant improvement in the quality of interaction.
Onboarding an AI Agent is akin to welcoming a new employee with boundless potential. Unlike traditional chatbots, AI Agents can instantly connect to your existing knowledge base, absorbing information in seconds. Once onboarded, the AI Agent empowers customers by reasoning through the best solution, operating like a human agent. It identifies relevant information, outlines clear steps to resolve the issue, and delivers a personalized solution.
AI agents and chatbots also differ in their purpose. While chatbots are designed to interact with humans, AI agents are designed to complete autonomous tasks. The most significant difference lies in their ability to take independent action. Since AI chatbots are primarily focused on human interaction, they are typically not programmed to act autonomously. Their purpose is to directly assist a human user.
This isn’t a futuristic concept; businesses leading the way in AI are already leveraging this technology today.
The Future of AI Agents
The AI era is just beginning, and its evolution is breathtaking. From the dawn of computers to the internet, from the first large language models to the emergence of advanced agent technology, technology continues to expand our world at an astonishing pace.
This evolution is poised to reshape the business landscape. Interacting with AI assistants is already commonplace in large organizations. As technology advances and agents become more capable of independently completing complex tasks, their scope will expand across industries.
The buzz surrounding AI agents is well-deserved. As they continue to evolve, they will be able to collaborate on increasingly complex tasks, reducing the need for extensive prompt engineering by users. For developers, the benefits are clear: AI agents free them to focus on higher-value activities.
When LLMs are equipped with tools, memory, and the ability to plan, they become akin to LEGO blocks, ready to be assembled into more sophisticated systems. AI agents, at their best, are modular, adaptable, interoperable, and scalable, just like LEGOs. Developers can use them to build multi-agent systems, promising to revolutionize software development.
At Cloudsway, we are excited about the potential of AI agents, agentic AI, and multi-agent systems for software developers. We invite you to build with Cloudsway or host your agents with us. Let’s embark on this journey together.