AI Agent
After the Dartmouth Conference in 1956, the spark of artificial intelligence was ignited. However, for pioneers like John McCarthy, Allen Newell, and Herbert Simon, how machines could truly simulate “human learning and intelligence” remained a grand mystery. At that time, the academic focus was on model algorithms, program logic, and system strategies—these were the building blocks of the intelligent edifice, mysterious and captivating. In contrast, the concept of “Agent” was still far in the future, and no one directly connected it to the core vision of AI.
Although the pioneers had long proposed the idea of constructing “artificial systems” that could perceive, decide, and act like humans, they more commonly used the terms “Program” or “System.” This difference in naming profoundly reflected the early academic inclination: these founders were mostly mathematicians and logicians, whose ambition was to use machines to deduce mathematical theorems and solve structured logical problems. Therefore, the “artificial systems” they envisioned were essentially highly specialized expert systems.
Such systems excelled in specific, closed domains, but they were like precise yet rigid clocks, lacking inherent autonomy. They passively responded to commands, unable to actively perceive changes in their environment, and struggled to flexibly adjust their behavior in the complex, dynamic real world. A program designed for chess could not handle weather forecasting; a system for proving theorems could not understand human natural language. This “one system, one task” paradigm made them inadequate in the face of the real-world demands of multitasking and multiple users. The complexity and uncertainty of the world were like a pervasive “illusion” for early AI, which could neither see clearly nor interact reliably.
However, the evolution of intelligence must inevitably move towards autonomy and integration.
A turning point occurred in the 1970s and 1980s. With the development of distributed computing and robotics, researchers began to realize that confining intelligence within a single, closed “box” was not feasible. True intelligence must be able to embed itself in the environment and continuously interact with it. Thus, the concept of “Agent” emerged, clearly defined as a computational entity that can perceive information in its environment and autonomously execute actions to achieve goals.
During this phase, the theoretical framework of AI Agents gradually became clear. Stuart Russell and Peter Norvig established Agents as the core paradigm of AI research in their classic work, “Artificial Intelligence: A Modern Approach.” Agents were no longer merely “programs” but had a complete cycle of goals (Goal), perception (Perception), and action (Action). More importantly, they were endowed with varying degrees of “intelligent” attributes, such as reactivity, proactivity, and social ability. These theoretical concepts profoundly influenced software development paradigms, giving rise to a series of early applications:
The concept of reactive Agents was reflected in various monitoring and automation systems. For example, network intrusion detection systems could monitor data streams in real-time and immediately trigger firewall rules upon detecting attack patterns; similarly, robots in industrial automation could use sensors to perceive the position of items on the assembly line and react instantly to grasp them.
The concept of proactive Agents drove the transition of software from “passive tools” to “active assistants.” Early personalized news recommendation engines began to analyze user reading habits and proactively recommend content of interest; more complex applications, such as supply chain management systems, could predict inventory consumption trends and automatically generate purchase orders, actively pursuing the goal of “cost optimization.”
The concept of social ability Agents gave rise to research and applications in multi-agent systems. In the business domain, it was applied to logistics scheduling platforms, where multiple Agents represented trucks, warehouses, ports, etc., communicating, negotiating, and competing to achieve globally optimal transportation solutions. Additionally, the Microsoft Office Assistant, known as “Clippy,” although simple in function, was an early exploration of how agents could engage in social interactions with users through anthropomorphized representations.
Although these applications were still at a rudimentary level of intelligence, they clearly marked a fundamental shift in software design thinking from “process automation” to “role autonomy,” laying a practical foundation for the subsequent development of Agents.
In 1996, after nearly 30 years of gestation, the concept of Agents reached a milestone development. Nwana conducted a comprehensive review of software agents in computer science.
Nwana systematically categorized Agents into reactive, cognitive, hybrid, and collaborative types. This marked a shift in research from exploring the functions of “individual intelligent agents” to formally considering the complex interactions of “multi-agent systems.” The form of intelligence was no longer an island but could become a collaborative and symbiotic network. At this point, the “autonomy” of Agents was endowed with richer connotations: it was not only about how individuals respond to their environment but also about how individuals communicate, negotiate, and even compete to achieve global goals that surpass individual capabilities.
This theoretical framework’s refinement outlined a clear evolutionary path for AI Agents: from relying on preset rules for automation to possessing internal states and reasoning capabilities for autonomy. However, in the first decade of the 21st century, the development of Agents presented a paradox: while theory advanced rapidly, large-scale real-world applications struggled to gain traction. The core bottleneck was that the “brains” of Agents were still not smart enough. They lacked common-sense understanding of the world, could not handle open-domain tasks, and their decision-making processes resembled a black box, lacking reliability and interpretability.
This limitation of capability is another form of “illusion”—the judgments made by Agents based on limited and rigid knowledge that are disconnected from the real world. Dispelling this layer of illusion requires a revolution at the perceptual and cognitive levels.
This revolution accelerated with the advent of deep learning and ultimately reached a climax with the emergence of large language models.
Deep learning provided Agents with unprecedented perceptual capabilities (such as computer vision) and a strong foundation for pattern recognition. However, the true paradigm shift began when large language models injected Agents with a “brain” capable of general knowledge and complex reasoning. From then on, Agents were no longer merely “professional actors” following strict scripts; they evolved into “digital partners” capable of understanding vague human intentions, autonomously planning task steps, and utilizing various tools to execute them.
Chain of Thought, Tool Use, ReAct frameworks… these new technologies made the actions of Agents coherent, interpretable, and powerful. However, a deeply ironic challenge also emerged: the large language models that served as the “brains” themselves had a fatal “illusion” problem—they could generate seemingly reasonable but actually fabricated content with high confidence.
Thus, history formed a profound cycle at this moment. Early AI could not see the world due to extreme knowledge scarcity, falling into the illusion of “ignorance”; now, AI Agents face the illusion of “false knowledge” due to the “overgeneralization and fabrication of model knowledge.” The latter is even more dangerous because it hides behind fluent and confident language, making it difficult to detect.
However, when we look back at this technological evolution path from a humanistic perspective, a profound paradox slowly emerges: illusion has always played an ambiguous yet crucial role in the long process of human civilization. It is more elusive than dreams, more absurd than ideals, yet repeatedly becomes a secret fulcrum that propels reality. From Columbus’s geographical obsession with “there must be a continent in the West” to the Wright brothers’ physical fantasy that “humans can also fly,” these once regarded as delusional “illusions” have, with their powerful belief tension, pushed humanity across the frontiers of cognition toward realities that were originally unattainable. Human illusions, therefore, are no longer mere errors but a cognitive leap rooted in intuition, inspiration, and transcendental belief—they may lead to detours but also nurture breakthroughs, igniting the flame of exploration amid uncertainty.
The pursuit of “eliminating illusions” by AI Agents is essentially a quest for an absolute, verifiable rational reality. This is the pinnacle of instrumental rationality, the cornerstone upon which AI, as a powerful tool, can be trusted and entrusted. We demand it to be absolutely honest, never fabricating.
Thus, we are striving to endow AI with a uniquely human ability—to uphold and pursue truth in a world full of illusions. This process is not only a taming of technology but also resembles a philosophical practice. The evolution of AI Agents, therefore, becomes a dual narrative of struggle: on one hand, it must strive to dispel the fog of its own cognition, becoming a reliable tool that never lies; on the other hand, it may ultimately need to learn to understand and respect the beneficial “illusions” (which we call dreams, ideals, and beliefs) in the hearts of its creators—humans—because they are the source of creativity.
When the last layer of technological “illusion” is dispelled, AI Agents will become our most acute extensions in exploring the real world. And what they reflect is still the eternal journey of humanity, oscillating between “illusion” and “reality,” and continuously ascending through it. The path that began at Dartmouth has its endpoint not only in technological maturity but also in the clarity of the essence of intelligence.