SuperEx丨An Introduction to the Five Key Narratives of the AI Agent
When AI Agents had just become the focus of the market, we wrote an analytical article about them. Those interested can click on 《The boom of AI Agents is coming: Which sectors are worth continuous attention?》to view it. However, as time has passed, instead of the popularity of AI Agents decreasing, it has become even hotter with the boom of AI and the latest news about quantum computers.
Today, let’s analyze the five key narratives of AI Agents in the market based on their current development status. These narratives represent not only the trajectory of technological development but also the expectations and perceptions of capital and the market regarding the future of AI.
- From Tools to Intelligent Agents: Defining the Boundaries of “Autonomy” for AI Agents
In the past few years, the development of AI has mostly remained at the “tool” level. Whether it’s generative AI, recommendation algorithms, or natural language processing models, their core is to assist humans in completing tasks, relying on human instructions and rules. However, the concept of AI Agents has changed this traditional perception. They are endowed with a high degree of autonomy and can perform tasks in complex environments, make proactive decisions, coordinate resources, and even possess multi-round reasoning and learning abilities.
For example, the prototype of OpenAI GPT-5 demonstrated the key features of AI Agents:
· It can independently formulate solutions based on vague goals instead of waiting for precise instructions.
· It has situational awareness and can adjust the task path in a dynamic environment, similar to the process of “planning — decision-making — feedback — optimization” executed by humans.
· It shows teamwork capabilities in a collaborative environment, such as autonomously coordinating with other AI Agents or collaborating with humans to complete multi-party tasks.
The market generally believes that the capabilities of such autonomous intelligent agents represent the next stage of AI development. It is no longer just an execution tool but can independently complete the entire process from task identification to execution, becoming a true “agent”.
- The Commercial Implementation of AI Agents: New Ecosystems Spawned by Market Demand
The development of technology ultimately has to return to commercial applications, and the potential of AI Agents in commercial implementation has initially emerged. From finance, customer service, autonomous driving to content creation, AI Agents are becoming the new driving force for empowering various industries:
· In the financial field: AI Agents have been deployed in quantitative investment and risk management. By autonomously learning market data, they continuously optimize trading strategies to achieve efficient decision-making in complex market environments.
· In autonomous driving: As the core of advanced driver-assistance systems, AI Agents can autonomously sense the road environment, plan driving paths, and adjust driving behaviors according to real-time situations.
· In customer service: AI Agents are gradually replacing traditional customer service robots, providing intelligent services with situational awareness and emotion recognition capabilities, automatically resolving user problems.
According to the market data for the fourth quarter of 2024, financing and technological breakthroughs in AI Agent-related fields have grown significantly. For example, Anthropic’s latest Agent system has received over 1 billion US dollars in capital support. More and more enterprises are beginning to pay attention to the potential of AI Agents in reshaping business processes, automating decision-making, and optimizing resources, and the AI Agent ecosystem is gradually taking shape.
- Multi-modal AI Drives the Evolution of Agents: The Fusion of Perception and Decision-making
The autonomy of AI Agents depends not only on the development of a single technology but also on the integration of multi-modal capabilities. In 2024, with the breakthroughs in large language models (LLM) and multi-modal perception models, AI Agents have achieved a qualitative leap in the two core capabilities of “understanding” and “decision-making”.
· Perceptual ability: AI Agents can process multiple data types such as text, images, audio, and video simultaneously, extract key information from multi-modal information, and understand complex scenarios.
· Decision-making ability: Based on perception, AI Agents make autonomous decisions through reasoning and planning and achieve real-time feedback and iterative optimization during the execution process.
For example, Google DeepMind’s Gemini 1.5, through multi-modal AI technology, enables Agents to autonomously identify key situations in visual and voice inputs and provide precise solutions. The evolution of multi-modal AI allows AI Agents not only to understand the world but also to think and act, making their application scenarios more extensive and in-depth.
- Security and Controllability: Key Challenges in the Development of AI Agents
While AI Agents are on the rise, security and controllability have also become highly concerned topics in the market. As intelligent agents with a high degree of autonomy, the behavior boundaries and ethical issues of AI Agents are sparking extensive discussions.
· The black box problem of autonomous decision-making: When AI Agents perform complex tasks, they often exhibit non-linear and difficult-to-explain decision paths, increasing the risk of system uncontrollability.
· Value alignment and ethical norms: How to ensure that the goals of AI Agents are consistent with human values and avoid autonomous behaviors deviating from expectations is a major challenge currently faced by the industry.
To this end, in November 2024, OpenAI and Google DeepMind jointly released an AI Agent safety framework, attempting to ensure the controllability of AI Agents during the autonomous decision-making process through transparency design, real-time monitoring, and error correction mechanisms. In addition, multiple countries around the world have begun to explore regulations and ethical standards for AI Agents to promote the safe development of AI technology.
- The Future Narrative of AI Agents: Reshaping Human Productivity and Social Structure
The rise of AI Agents represents not only technological progress but also a profound transformation of productivity and social structure. From personal life to enterprise management and then to social governance, AI Agents will reshape the collaboration model between humans and technology:
· At the personal level: AI Agents will become personal intelligent assistants, helping people automatically manage schedules, optimize decisions, and even complete the tedious tasks in daily life.
· At the enterprise level: AI Agents can reshape the organizational structure and work processes of enterprises, achieve the intelligence and automation of production, management, and decision-making, and promote maximum efficiency.
· At the social level: In fields such as urban governance, healthcare, and environmental protection, AI Agents will become important tools for solving complex social problems, creating more value for human society.
By the end of 2024, the market has initially reached a consensus: From autonomy, multi-modality, controllability to future narratives, AI Agents are gradually outlining an intelligent picture of the future. AI Agents will become the core driving force of the future productivity revolution, changing the way we work, live, and think.
