{"id":22761811,"date":"2024-10-10T06:16:57","date_gmt":"2024-10-10T13:16:57","guid":{"rendered":"https:\/\/thenewstack.io\/?p=22761811"},"modified":"2024-11-12T10:31:15","modified_gmt":"2024-11-12T18:31:15","slug":"how-ai-agents-are-about-to-change-your-digital-life","status":"publish","type":"post","link":"https:\/\/thenewstack.io\/how-ai-agents-are-about-to-change-your-digital-life\/","title":{"rendered":"How AI Agents Are About To Change Your Digital Life"},"content":{"rendered":"\n

Imagine learning a new skill or understanding a complex concept, only to forget it entirely the moment you step away. Then when you need that knowledge again, it’s gone and you have to start from scratch. Frustrating, right? This lack of continuity would make it nearly impossible to build on your experiences or tackle increasingly complex tasks.<\/p>\n

AI agents<\/a> face a similar problem. They can process information, answer intricate questions and handle multistep workflows, but without a way to retain what they’ve learned, they start each interaction with a blank slate. For these agents to perform effectively, they need a memory system that allows them to recall and build upon past interactions. This is where vector databases<\/a> come in. Milvus<\/a>, an open source vector database<\/a> created by Zilliz<\/a>, enables AI agents to store, manage and retrieve high-dimensional data efficiently, giving them the memory they need to make smarter decisions and adapt over time.<\/p>\n

Let’s delve into what AI agents are and how vector databases like Milvus<\/a> enhance these systems to unlock their full potential.<\/p>\n

Understanding AI Agents<\/h2>\n

AI agents<\/a> are software entities designed to perform tasks autonomously. They are driven by complex algorithms and can interact with their environment, make decisions and learn from experiences. These agents are employed in various applications such as chatbots, recommendation systems and autonomous vehicles.<\/p>\n

At their core, AI agents operate through a cycle of perception, reasoning, action, interaction and learning.<\/p>\n

\"An

Structure of an intelligent agent<\/p><\/div>\n

Perception<\/h3>\n

The process begins with AI agents gathering information from their surroundings through sensors or user inputs. For instance, a chatbot processes text from a conversation, while autonomous vehicles analyze data from cameras, radar or lidar sensors. This gathered data forms the agent’s perception of its environment, setting the stage for informed decision-making. The accuracy of this perception is crucial as it significantly impacts the quality of subsequent actions and interactions.<\/p>\n

Reasoning<\/h3>\n

Once data is collected, AI agents process and analyze it to derive meaningful insights. This stage involves using large language models<\/a> or rule-based systems to interpret the input, identify patterns and contextualize the information. The reasoning process is also influenced by the agent’s world-knowledge memory, allowing it to leverage past experiences for improved decision-making. For example, in a recommendation system, the agent analyzes user preferences and behavior to suggest relevant content. Reasoning is critical for understanding the environment and predicting the consequences of potential actions.<\/p>\n

Action<\/h3>\n

Following the reasoning phase, the agent takes action based on its analysis. This might involve responding to a user query in a chatbot, suggesting a product in an online store or making a steering adjustment in an autonomous vehicle. The actions are not isolated events; they are direct outputs of the agent’s reasoning process. Effective actions rely on accurate perception and sound reasoning to ensure the agent can perform its intended tasks successfully.<\/p>\n

Interaction<\/h3>\n

Beyond singular actions, AI agents often engage in continuous interaction with their environment and users. Interaction is a more dynamic form of action where the agent repeatedly exchanges information with the external world. This ongoing dialogue allows the agent to refine its understanding and adjust its behavior in real time. For instance, in a conversational AI, the interaction involves maintaining context over multiple exchanges, adapting responses based on user feedback and providing a coherent experience. This iterative exchange is crucial for environments that change frequently or require complex decision-making over time.<\/p>\n

Learning<\/h3>\n

Learning distinguishes AI agents from traditional software. After taking action and interacting with the environment, the agent evaluates outcomes and adapts its future behavior. This learning process is driven by feedback loops, where the agent learns from its successes and failures. By integrating the knowledge memory, the agent continually updates its understanding of the environment, making it more adept at handling new and unexpected scenarios. For example, an autonomous vehicle improves its navigation by analyzing previous driving conditions, and a recommendation system refines its suggestions based on user feedback. This continuous learning cycle ensures that AI agents become more effective and intelligent over time.<\/p>\n

While these stages outline the fundamental workings of an AI agent, their true potential is unlocked when they can store and retrieve knowledge in the long term, enabling them to learn from past experiences and adapt. This plays a pivotal role in enhancing these agents’ memory and decision-making capabilities.<\/p>\n

How Vector DBs Empower AI Agents<\/h2>\n

Vector databases (DBs) are specialized databases<\/a> optimized to handle high-dimensional vectors, which are numerical representations of complex data like text, images and audio. Unlike traditional databases that store structured data, vector DBs store vectors to facilitate similarity searches, which is essential for tasks like information retrieval and recommendation. Milvus is an open source<\/a> vectorDB designed specifically for these requirements, providing a scalable and efficient solution. It is the most popular vector database in terms of GitHub stars.<\/p>\n

Vector DBs like Milvus serve as a memory system for AI agents, enabling them to handle vast amounts of high-dimensional data efficiently. It’s important to note that not all vector DBs are the same. It’s important to pick one with comprehensive search features and that is highly scalable and performant. Vector DBs with these types of features, such as Milvus, are key to building more intelligent AI agents.<\/p>\n

Building Long-Term Memory<\/h3>\n

Agents rely on long-term memory to retain information and context across interactions. They must have access to an efficient way to store and retrieve semantic data:<\/p>\n