Time is like a pair of scissors, cutting our rapidly developing technological era into segments and tagging them—information, digital, internet, big data, artificial intelligence, etc., seemingly getting closer to the essence of digital civilization. The advancing digital civilization has never been absent from the main line of historical development and the trajectory of history.
1. From Traditional Software to Intelligent Agents
Before the explosion of large AI models, software developed based on computer systems was composed of code and data, serving as a digital application tool where we input commands, “do what I say.” The service capability is determined by the executing code and the backend database, which cannot derive any other capabilities. For example, the apps we frequently use on our phones are applications where you want to shop, hail a ride, book a room, or order food… it’s a question-and-answer interaction, and if it exists, it exists; if not, it doesn’t. At most, it adds some recommendations, potential likes, and algorithms that seem to exploit human weaknesses, and that’s it.
In the field of artificial intelligence, an intelligent agent known as “Agent” has gained fame overnight alongside large models. Understanding it from the English term “Agent” means a representative. Although it is an agent, since it is a “person,” it must possess wisdom. Where does this wisdom come from? From the current situation, it appears that there are “masters” behind the intelligent agents, supported by three “greats”—big data, large models, and massive computing power.
Whether it’s AgentGPT, AutoGPT, OpenAI’s GPTs, or other intelligent agents like Doubao, Kimi, and DeepSeek, for ease of understanding, we refer to them as intelligent assistants. They are supported by vast amounts of data and supercomputing power, driving the transformation of traditional software applications from “pure tools” to “autonomous intelligent agents.” The traditional software industry will face new challenges and may be restructured, moving towards a new height of development.
So, what exactly is an intelligent agent? By definition, an intelligent agent is an intelligent entity capable of perceiving the environment, making autonomous decisions, executing complex instructions, and independently completing multi-step operations to achieve task objectives. Essentially, it is a system driven by large language models (LLMs), utilizing data governance and high-quality dataset construction, and completing complex tasks through integrated planning, memory, and tool invocation capabilities. In simpler terms: intelligent agent = large language model (brain) + memory system + tool invocation + planning capability.
Compared to traditional software applications, the core of a successful intelligent agent lies in planning, memory, and tool invocation.
Planning involves breaking down complex tasks into executable smaller tasks and dynamically adjusting the execution path, balancing the agent’s flexibility and determinism. Memory is divided into short-term and long-term; short-term memory temporarily stores contextual information such as dialogues during task execution, while long-term memory requires the establishment of an external knowledge base, which can store industry knowledge and expert knowledge in a vector database, and be dynamically updated to ensure the accuracy, timeliness, and completeness of knowledge. Tool invocation involves integrating external resources such as APIs and RPA robots to enhance the capabilities of intelligent agent applications. For example, NetEase’s CoreAgent enhances its functionality by invoking e-commerce platform interfaces to analyze online products.
Currently, a prominent feature of intelligent agent applications is the shift from single-task execution to cross-application, multi-task collaboration, enhancing multimodal interaction capabilities with text, images, audio, and video, and integrating and improving perception methods and means, resulting in a qualitative leap in their application “IQ.”
2. The Broad Prospects of Intelligent Agent Applications
From the perspective of industry application scenarios for intelligent agents, the development prospects in enterprise services, smart homes, finance, healthcare, and education are very promising, especially in the healthcare sector.
Reports indicate that the application scenarios of intelligent agents in healthcare mainly include precise diagnosis and assisted treatment: firstly, intelligent analysis of medical images, where intelligent agents based on deep learning technology can quickly identify abnormal areas in CT, MRI, and other images (such as tumors and vascular lesions), quantifying and accurately analyzing the size and morphological characteristics of lesions, potentially increasing diagnostic accuracy to over 95%. Secondly, multimodal data fusion diagnosis and treatment, where intelligent agent platforms integrate electronic medical records, genetic data, and vital sign monitoring information to provide effective personalized treatment recommendations for doctors, significantly reducing the risk of misdiagnosis in complex cases.
Each intelligent agent requires support from large models and big data. Recently, Westlake University released a multimodal pathological large model, which took five years to develop, starting from mining vast amounts of literature and data, achieving remarkable research results: scanning slices with an accuracy of 0.25 microns, combining genetic data, electronic medical records, and other materials to complete auxiliary screening for 40 types of cancer, such as lung cancer and breast cancer, within seconds, intelligently marking suspicious lesions, thereby enhancing the efficiency of medical diagnosis, bringing new hope for humanity to conquer cancer and safeguard health.
There are many other applications of intelligent agents in healthcare, such as automated medical record generation and quality control, screening clinical trial subjects, drug efficacy prediction, dynamic health monitoring and early warning, etc. Healthcare intelligent agents are becoming “general practitioners” in the “prevention-diagnosis-treatment-rehabilitation” chain. In the future, the innovative applications of large models and intelligent agents will bring a new transformation to the traditional healthcare field, unleashing tremendous potential and value.
However, healthcare data involves a large amount of personal privacy, ethical risks, and security issues. Ensuring the security of patient information during the processing and use of intelligent agents is an urgent problem that needs to be solved; at the same time, it is necessary to consider the iterative update of medical knowledge, the application of the latest medical research results, and clinical practice experience to enhance the accuracy and reliability of medical diagnosis, as well as the potential liability issues arising from autonomous decision-making errors by intelligent agents.
Therefore, creating a safe and trustworthy intelligent agent is particularly important: firstly, scenario design: clearly define what problems the intelligent agent is designed to solve, what the requirements are, and the expected outcomes, sorting and confirming the list of problems and needs to be addressed. Secondly, data preparation: collect, clean, and label data, providing high-quality datasets and corpora for the large model through governance, correction, and alignment to ensure effective pre-training. Thirdly, model adaptation: design, train, and optimize algorithms, selecting appropriate large models and corresponding model parameter ratios, focusing on being “small and precise” rather than “large and comprehensive.” Fourthly, result validation: based on the open interface capabilities provided by large models, design and develop intelligent agents, combining datasets and third-party service capabilities, and improving the agents through result validation and security testing.
Some predict that a new type of data operation company focused on the operation of intelligent agents will soon emerge, increasingly tending towards “small and precise” and “specialized and deep.” Companies developing and operating intelligent agents may consist of only two or three people in one office, and such companies will become more common, supported by a professionalized and scaled operation team or large AI companies that provide massive data, supercomputing power, and large model service capabilities. The traditional software industry will face challenges or transformations, breaking the existing pattern and reshaping it.
In the AI era, intelligent agents and large models will achieve deep integration and innovative applications in more industry fields. Based on multi-agent business collaboration and data-driven approaches, in the transportation sector, they will promote more efficient optimization of traffic flow control and assist in autonomous driving; in the education sector, they may create intelligent learning assistants that better fit each student’s characteristics; in industrial manufacturing, they will help make production processes more automated and intelligent, further improving production efficiency and product quality; in the intelligent customer service sector, agents can understand user intentions and needs, automatically answer user questions, provide 24-hour uninterrupted service, and enhance user satisfaction and loyalty. In the smart home sector, intelligent agents can interact with each other to easily manage lighting, temperature, security, and other devices, enhancing comfort and convenience in life.
3. The Future Development Space of Embodied Intelligent Agents
It is foreseeable that intelligent agents are not merely a simple iterative upgrade from traditional applications to intelligent applications, but a beautiful transformation of traditional applications, a historically significant leap and evolution.
Intelligent agents can be seen as the intelligent “variant” of traditional software, running on computers, phones, iPads, and other terminals, also known as non-embodied intelligent agents. In the future, there will be another type of intelligent agent with a “silicon-based life” body, called embodied intelligent agents, which refers to intelligent systems that have a physical form or exist in physical/simulated environments, capable of perceiving the environment, understanding tasks, planning, and executing actions to interact with the environment to achieve specific goals.
This type of embodied intelligent agent will inevitably penetrate into human production and life, becoming our assistants, friends, and indispensable partners. Currently, the most typical examples are embodied intelligent robots, such as humanoid robots, industrial robots, service robots, and other embodied intelligent agents like drones, autonomous vehicles, and smart appliances with certain autonomous decision-making and action capabilities. One day, you may have multiple embodied intelligent agents at home, following your commands to handle chores, assist with work, and you won’t have to worry about anything.
The difference between embodied intelligent agents and non-embodied intelligent agents (such as software applications) lies in “embodiment” and “interactivity.” Firstly, embodiment: the intelligent agent has a specific “body” existing in a certain environment, equipped with sensors to perceive the environment and actuators to interact with it. Secondly, interactivity: intelligent agents do not passively process data but actively and continuously engage in closed-loop interactions with their environment.
Embodied intelligent agents have a wide range of application fields, such as: robotics technology: warehousing logistics, home services, healthcare, hazardous environment operations, agriculture, space and deep-sea exploration; autonomous driving: autonomous vehicles are typical embodied intelligent agents; human-computer interaction: more natural and physical interaction methods (such as social robots, collaborative robots); smart homes/IoT: intelligent devices that can actively perceive the environment and perform physical tasks; VR virtual reality/AR augmented reality: intelligent agents that can physically interact with users and environments in virtual or mixed realities; gaming and simulation: creating intelligent, realistic NPCs or training simulation environments, etc.
Embodied intelligent agents represent an important direction for the future development of artificial intelligence, aiming to enable AI to step out of the purely digital world and possess the ability to “exist” and “act” in physical or simulated worlds, marking a key step towards more general and practical artificial intelligence. Research and development of an embodied intelligent agent require the integration of knowledge and achievements from multiple fields, including robotics, computer vision, natural language processing, machine learning, and cognitive science.
Ray Kurzweil, Google’s chief futurist, predicted that artificial intelligence will surpass human intelligence by 2029 and achieve human-machine symbiosis by 2045, reaching the so-called “singularity,” where AI will become part of our brains. He believes that at that moment, the level of artificial intelligence will increase by a million times, and humanity will achieve unprecedented leaps through the fusion with machines.
With the arrival of general artificial intelligence (AGI) and the new generation of world models, imagine if intelligent agents, whether embodied or non-embodied, could do everything that humans can do, what would humanity, the greatest “intelligent agent,” be capable of? What would the world become? A world without boundaries, or a flourishing one with diverse competition?
It seems that adapting to the rapidly changing AI era, seizing the opportunities presented by this era, and achieving a seamless connection between human natural intelligence and artificial intelligent agents, allowing humanity to be wiser and more in control of the future, may be an unavoidable compulsory course for everyone.@