The wave of artificial intelligence is surging, and AI Agents are like the intelligent helmsmen of the future, guiding the course of technological innovation. They are no longer simple lines of code but intelligent entities capable of autonomous learning, decision-making, and task execution. They exhibit intelligence that surpasses conventional norms in perception, decision-making, and execution, possessing multiple capabilities such as self-awareness, goal-setting, and dynamic adjustment. The emergence of AI Agents marks a new era in human technological development.
How do AI Agents differ from traditional software? Their core differences lie in autonomy, adaptability, learning ability, intelligent behavior, and goal orientation. AI Agents can independently plan and execute tasks without explicit instructions, whereas traditional software typically runs strictly according to preset programs. They can dynamically adjust strategies and behaviors based on environmental changes and feedback, which traditional software struggles to adapt to. AI Agents can continuously improve their performance through machine learning and experience accumulation, while traditional software lacks self-learning capabilities. They exhibit intelligent behaviors such as reasoning, learning, adaptation, and planning, utilizing artificial intelligence technologies to achieve these actions.
The emergence of AI Agents not only enhances work efficiency but also brings profound social and economic impacts on multiple levels. They can automate repetitive and time-consuming tasks, freeing up human resources to focus on more creative and strategic work. AI Agents can analyze vast amounts of data, identify patterns and trends, and provide decision-makers with more accurate and comprehensive information support. They can dynamically adjust resource allocation based on real-time data, improving resource utilization and reducing operational costs. AI Agents can facilitate cross-domain collaboration, stimulate innovative thinking, and accelerate technological breakthroughs. The emergence of AI Agents is reshaping business models; for instance, customer service can achieve 24/7 online support through AI Agents without a large human workforce.
From early mechanical automatons to recent chatbots, scientists and engineers have been pursuing AI systems that can work and act intelligently like humans. In recent years, breakthroughs in technologies such as large language models (LLMs), machine learning (ML), and natural language processing (NLP) have ushered in a new peak for the development of AI Agents. Early exploratory AI systems primarily focused on rules and logical reasoning, lacking autonomy and adaptability. With the advancement of machine learning, AI systems began to acquire the ability to learn from data, but still predominantly relied on human intervention. Breakthroughs in deep learning technology have enabled AI systems to handle more complex tasks, such as image recognition and natural language understanding. Recently, AI Agents based on large language models and reinforcement learning have emerged, exhibiting stronger autonomy, reasoning capabilities, and execution abilities. Now, people are exploring how to build multimodal, multi-agent systems that allow multiple agents to work collaboratively to solve more complex problems.
The key features of AI Agents include autonomy, perception, reactivity, reasoning and decision-making, learning, communication, goal orientation, tool use, and planning. A typical AI Agent usually consists of perception modules, cognitive modules, action modules, memory modules, learning modules, and collaboration modules. The working principle of AI Agents can be summarized as a cyclical process that encompasses perceiving the environment, processing information, making decisions, and executing tasks, continuously optimizing their behavior through feedback. The core is to achieve autonomy and intelligence to accomplish specific goals.
The realization of AI Agents involves several key technologies that collectively support their complete functional chain from perception to decision-making to execution. These technologies include perception technology, decision-making and reasoning technology, execution and control technology, learning technology, environmental interaction technology, and data management and processing technology. By integrating these key technologies, AI Agents can possess the capabilities of perception, reasoning, learning, and execution, widely applied in autonomous driving, medical diagnosis, intelligent customer service, industrial robotics, and more.
Major application scenarios for AI Agents across various fields include business and sales, finance, retail and e-commerce, healthcare, education, cybersecurity, content and marketing, manufacturing and logistics, urban planning, and software development. Several leading companies are pioneering AI agent technologies, such as Microsoft’s Copilot Studio, Salesforce’s Agentforce, Google’s Gemini 2 project, OpenAI’s Operator, and Anthropic’s Claude 3.5 Sonnet. These platforms lower the development threshold for AI Agents, allowing more people to participate in building AI Agents.
Building an AI Agent typically requires defining objectives, data collection and preparation, selecting platforms or frameworks, designing agent architecture, developing core algorithms, training the AI Agent, integrating with user interfaces, testing and debugging, deploying the AI Agent, and monitoring and updating. The difference between AI Agents and Agentic AI is that AI Agents are intelligent entities capable of perceiving the environment, making decisions, and taking actions, with a certain degree of autonomy and adaptability. Agentic AI is an advanced form of AI Agents, possessing a higher degree of autonomy, adaptability, and decision-making capability, able to operate in more complex environments.
Share
Save
View
Like
