Introduction: This afternoon, I arranged to have tea with Wu Da Si Ge and Li Huizhang from Wuhan Textile University. While waiting for them to arrive, I would like to share some of my personal thoughts on the currently popular concept of AI Agents.
1. Differences between AI Agents and Agentic AI.
They are two related but distinct concepts, with the core difference lying in perspective and scope:
1. AI Agent (Artificial Intelligence Agent)
Refers to a specific intelligent entity capable of autonomous decision-making, perceiving the environment, and executing tasks. It leans more towards the “individual” or “tool” aspect, emphasizing the functional attributes of a single intelligent agent, such as a navigation robot that can autonomously plan its path or a chatbot that automatically replies to messages. It is an independent intelligent unit aimed at achieving specific goals.
2. Agentic AI (Agent-based Artificial Intelligence)
Leans more towards the “paradigm” or “system” aspect, referring to artificial intelligence systems or technical frameworks built around agents. It emphasizes the collaboration and interaction of multiple agents to achieve complex functions, focusing on the organization and coordination mechanisms between agents, such as the application of multi-agent systems in traffic scheduling and group decision-making, reflecting a holistic design approach based on agents.
3. In simple terms, AI Agent is a “single intelligent tool,” while Agentic AI is a “systematic approach based on agents.”
2. In the rapidly developing field of artificial intelligence, AI Agents and Agentic AI have become prominent concepts. They not only drive the forefront of technological development but also profoundly change the operational models of many industries. From everyday applications to complex industrial processes, their presence is evident. A deep exploration of the principles, applications, challenges, and future directions of both is significant for understanding the current state and future trends of artificial intelligence.
3. AI Agent: Principles and Core Features AI Agents are intelligent entities capable of autonomously perceiving, deciding, and executing tasks in specific environments. They can be software programs, such as intelligent voice assistants; hardware devices, like industrial robots; or even virtual entities, like digital humans. The core aspect is autonomy, meaning they can adjust their behavior based on environmental changes to achieve preset goals without real-time human intervention.
1. Autonomy: AI Agents can operate and make decisions independently. For example, an intelligent air conditioner can automatically adjust cooling or heating modes and fan speed based on indoor temperature, humidity, and user preferences, maintaining a comfortable indoor environment.
2. Reactivity: Real-time response to environmental changes is a key capability. For instance, an AI Agent in navigation software can quickly adjust route planning based on real-time traffic information, providing users with optimal travel solutions.
3. Proactivity: Actively advancing goal achievement. An AI Agent in a recommendation system analyzes user behavior data and browsing history to proactively suggest relevant products or content.
4. Sociality: The ability to collaborate with other agents or humans. In drone formation performances, multiple AI Agents communicate and collaborate to precisely control flight posture and position, completing complex formation maneuvers.
5. Adaptability: Continuously optimizing behavior through experiential learning. Language model AI Agents like ChatGPT can improve response content and quality based on extensive user inquiries and interaction feedback.
4. The technical implementation of AI Agents relies on several key technology modules:
1. Perception Technology: Utilizing sensors (such as cameras, radar, microphones, etc.) or data interfaces to obtain environmental information. In the field of autonomous driving, AI Agents in vehicles capture road images through cameras and use radar to perceive the distance and speed of surrounding objects, thereby constructing a model of the traffic environment.
2. Decision Algorithms: These can be rule-driven or based on machine learning and deep learning. Rule-driven decisions trigger actions based on preset conditions, such as a smoke alarm sounding when smoke concentration exceeds a set threshold. Machine learning and deep learning optimize models through extensive data training, such as using the Naive Bayes algorithm for spam classification or LSTM networks for speech recognition.
3. Execution and Interaction: AI Agents complete tasks by invoking various tools, such as calling an airline ticket booking API to make reservations. They also achieve natural interaction with humans through natural language processing (NLP) technology, such as smart speakers receiving voice commands and executing corresponding actions.
5. Agentic AI: Paradigm Shift and Key Elements Agentic AI is not merely a single intelligent agent concept but represents a paradigm shift, emphasizing multi-agent collaboration, dynamic task decomposition, persistent memory, and coordinated autonomy.
1. Multi-Agent Collaboration: Multiple AI Agents collaborate through communication protocols to solve complex problems. In a smart factory, different robot AI Agents on the production line are responsible for material handling, parts assembly, and quality inspection, working together to ensure the efficient operation of the entire production process.
2. Dynamic Task Decomposition: When faced with complex tasks, Agentic AI systems can dynamically decompose them into multiple sub-tasks and assign them to suitable agents for execution. For example, in a large construction project, an intelligent project management system can break down the entire construction process into design, construction, procurement, and further subdivide into specific tasks like foundation work and main structure construction, coordinating different agents or teams to complete them.
3. Persistent Memory: The system has the ability to store and utilize historical information for long-term planning and learning. In the medical field, intelligent diagnostic systems can save patients’ historical medical records and test results, providing references for subsequent diagnoses and treatments, and improving diagnostic accuracy and reliability through learning from extensive case data.
4. Coordinated Autonomy: Each agent maintains a certain level of autonomy while achieving overall goals through coordination mechanisms. In intelligent transportation systems, different vehicle AI Agents must make autonomous decisions based on their driving conditions, such as accelerating, decelerating, or changing lanes, while coordinating with surrounding vehicles and traffic infrastructure agents to avoid congestion and improve road efficiency.
6. Application Areas: Widespread Penetration and Deep Transformation AI Agents have diverse applications. With their unique capabilities, AI Agents play significant roles in various fields:
1. Customer Service: Intelligent customer service systems are widely used in e-commerce, finance, telecommunications, and other industries. These AI Agents can respond to user inquiries in real-time, answer common questions, and handle simple tasks, achieving 24/7 online service, greatly improving customer service efficiency and reducing labor costs. For example, many banks’ online customer service robots can quickly answer users’ questions about account inquiries, money transfers, and financial products.
2. Automated Task Execution: In office settings, AI Agents can automatically complete repetitive tasks such as data entry, form processing, and email classification. For instance, AI Agents can automatically extract key information from numerous invoices and input it into financial systems, reducing the tediousness and errors of manual operations.
3. Smart Home Control: Through voice commands or mobile applications, AI Agents can control smart devices in the home, such as lights, air conditioning, televisions, and curtains. For example, Amazon’s Echo smart speaker allows users to interact with the built-in AI Agent via voice, facilitating convenient control of smart home devices and enhancing comfort and convenience in life.
4. Autonomous Driving: AI Agents are the core of autonomous vehicles, capable of processing data from cameras, radars, and other sensors to perceive traffic conditions in real-time, making driving decisions, and achieving autonomous navigation, obstacle avoidance, and following functions. Tesla’s Autopilot and Google’s Waymo autonomous driving technology are typical applications of AI Agents in this field.
5. Content Generation: In creative fields, AI Agents can assist in generating text, images, music, and other content. Text generation models like the GPT series can help users write articles, stories, and code; image generation models like DALL·E can create corresponding images based on user descriptions, providing inspiration and support for artistic creation and design.
7. Cutting-edge Applications of Agentic AI Agentic AI demonstrates immense potential in more complex and advanced fields:
1. Research Automation: In the scientific research field, Agentic AI systems can coordinate multiple agents to complete tasks such as literature retrieval, data analysis, and experimental design. For example, in drug development, different agents can be responsible for screening potential drug targets, simulating interactions between drug molecules and targets, and designing experimental plans to verify efficacy, accelerating the drug development process.
2. Robot Collaboration: In industrial manufacturing and logistics, multiple robot AI Agents collaborate to complete complex tasks. In logistics warehouses, handling robots, sorting robots, and stacking robots work together to achieve efficient storage, sorting, and delivery of goods, improving the efficiency and accuracy of logistics operations.
3. Medical Decision Support: In the medical field, Agentic AI systems can integrate multi-source information such as patient medical records, genetic data, and imaging data, with multiple agents collaborating to analyze and provide doctors with more comprehensive and accurate diagnostic suggestions and personalized treatment plans. For example, by analyzing extensive medical data, agents can help doctors predict disease progression and assess the effectiveness and risks of different treatment options.
8. Challenges Faced: Technical Bottlenecks and Application Dilemmas Challenges of AI Agents:
1. The “hallucination” problem: AI Agents may generate seemingly reasonable but completely incorrect information when dealing with complex problems or incomplete data, which is very dangerous in fields requiring high accuracy, such as finance and healthcare. For example, in financial investment advice, if an AI Agent provides erroneous market analysis and investment strategies, it could lead to significant losses for investors.
2. Limitations in Causal Understanding: Lacking a deep understanding of causal relationships, AI Agents struggle to cope with new situations and complex scenarios that differ from training data. In autonomous driving, if faced with rare road conditions or special traffic events, AI Agents may fail to make accurate judgments and optimal decisions.
3. Insufficient Capability for Complex Task Handling: For complex tasks requiring long-term planning and multi-step coordination, AI Agents often perform inadequately. For example, in project management, complex projects involving multiple stages, various resources, and numerous personnel may challenge AI Agents in effectively allocating tasks, tracking progress, and controlling risks.
4. Imperfections in Autonomy and Sociality: Although AI Agents possess a certain level of autonomy and sociality, there remains a significant gap compared to humans, leading to potential communication issues and goal inconsistencies in collaboration and interaction with humans.
9. Challenges of Agentic AI:
1. Amplified Causal Issues: Due to multi-agent collaboration, an error or misjudgment by one agent can trigger a chain reaction and cascading errors throughout the system, severely threatening stability and reliability. In smart grids, if a power scheduling agent in a certain area malfunctions or misjudges, it could affect the stability of the entire grid, leading to widespread power outages.
2. Coordination and Communication Bottlenecks: Inconsistencies in goal understanding among multiple agents and non-standard communication protocols may lead to coordination difficulties and communication inefficiencies. In complex tasks involving multi-robot collaboration, communication delays or misunderstandings among robots may cause confusion in their cooperation, preventing timely task completion.
3. Emergent Behavior and Unpredictability: As the number of agents and system complexity increases, unpredictable emergent behaviors may arise, potentially leading to unexpected outcomes or safety risks. In intelligent transportation systems, interactions among numerous vehicle AI Agents may result in unforeseen traffic congestion patterns or accident hazards.
4. High Complexity in Scalability and Debugging: Agentic AI systems face performance degradation and excessive resource consumption when scaled to large applications. Additionally, due to system complexity, debugging and error tracing become extremely challenging, making it difficult to identify the root causes of errors.
10. Future Prospects: Technological Breakthroughs and Industry Transformation Future Development Directions of AI Agents:
1. Improvements in Reinforcement Learning and Optimization Algorithms: Enhancing reinforcement learning algorithms to improve AI Agents’ decision-making capabilities and learning efficiency, enabling them to better cope with complex environments and dynamic tasks. For example, developing more efficient reward mechanisms and policy search algorithms to allow AI Agents to find optimal action plans more quickly.
2. Enhanced Multimodal Fusion and Understanding Capabilities: Further strengthening AI Agents’ ability to integrate and understand multimodal information such as text, images, speech, and video, achieving more natural and intelligent interactions. For instance, in intelligent customer service, AI Agents can not only understand users’ text inquiries but also analyze users’ vocal tones and facial expressions to more accurately grasp their emotions and intentions.
3. Deep Integration with Human Collaboration: Researching how to better integrate AI Agents into human work and life scenarios, achieving seamless human-machine collaboration. For example, in medical surgeries, AI Agents can serve as auxiliary tools for doctors, providing real-time surgical risk assessments and operational suggestions, collaborating with doctors to complete surgeries.
11. Future Prospects of Agentic AI:
1. Building Large-Scale, Efficient Multi-Agent Systems: With technological advancements, it will be possible to construct larger, more complex, and efficient multi-agent systems for large-scale complex scenarios such as urban management, global supply chain optimization, and ecological environment monitoring. For instance, in urban traffic management, coordinating numerous vehicles, traffic lights, and public transport systems can achieve intelligent regulation of urban traffic, alleviating congestion.
2. Promoting Cross-Domain Application Innovations: Agentic AI will achieve innovative applications in more fields, such as education, energy, and agriculture. In education, multiple agents can be responsible for analyzing students’ learning situations, formulating personalized learning plans, and recommending teaching resources, providing more precise and efficient educational services for students.
3. Facilitating the Integration of Artificial Intelligence with Other Technologies: Integrating with blockchain, the Internet of Things, quantum computing, and other technologies to expand the application boundaries and capabilities of Agentic AI. For example, leveraging the decentralized and secure characteristics of blockchain technology to enable more trustworthy collaboration and data sharing among multiple agents; utilizing IoT technology to achieve broader and more real-time perception and control of the physical world by agents.
12. AI Agents and Agentic AI, as important development directions in the field of artificial intelligence, are undergoing rapid development and transformation. Despite facing numerous challenges, with continuous technological advancements and innovations, they are expected to bring more convenience and transformation to human society in the future, driving various industries towards intelligence and efficiency. We need to continuously monitor their development dynamics, actively address challenges, and fully explore their potential to achieve harmonious coexistence and mutual progress between artificial intelligence and human society.
13. In summary:
1. Currently, it is quite difficult for artificial intelligence to monetize. The general public can only focus on, learn, and enhance their understanding of AI. For those looking to invest in artificial intelligence, I advise against casual investments, as it could lead to significant losses.
2. The year 2025 is considered the inaugural year for AI Agents in China, but with more than half of this year already gone, I have yet to see a domestic AI Agent that I would be willing to pay for, indicating that the road for AI Agents in China remains long and arduous.
3. Some AI Agents in the United States have indeed begun to monetize, but none have emerged in China yet.
4. The recently released GPT-5 by OpenAI has not met expectations and has not been impressive.
Mei Lingfeng, August 11, 2025, in Guangzhou