#01-What Is an AI Agent?
You can think of it as a super-smart robotic assistant (like Iron Man’s Jarvis), with a powerful brain and learning ability. This robotic assistant can not only understand human language but also continuously improve its skills in a specific area through learning and data analysis.
Currently, most of the LLMs (large language models) we use operate in a zero-shot manner, similar to writing an essay during an exam. After we input prompt information in the dialogue box, the model outputs results based on the prompt information in one go, without making any “corrections” during the process.
With the introduction of the agent workflow, we can require LLMs to process the prompt’s objectives through multiple iterations, resulting in higher quality outcomes, akin to how we privately revise our writing repeatedly.
#02-Background of AI Agents
The concept of AI agents originates from developments in the field of artificial intelligence. Initially, AI research focused on enabling machines to perform simple mathematical calculations and logical reasoning. With advancements in computing technology, especially in data processing capabilities and algorithms, modern AI agents can handle more complex tasks such as language, vision, and even emotional interaction.
#03-Current Research Status of AI Agents
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In the collaborative office field, the application of AI agents is particularly prominent. Both domestic and foreign companies are actively introducing AI agents into collaborative office scenarios to improve work efficiency and reduce labor costs. For example, some AI agents can assist with document editing, meeting scheduling, email replies, etc., significantly reducing employee workload. 
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In the entertainment field, AI agents also play an important role. For example, some AI chatbots can provide users with personalized entertainment experiences, including chatting, storytelling, and playing music. -
AI agents can also be used in the online education field to provide students with intelligent learning assistance and Q&A services. 
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In the paper titled “Generative Agents: Interactive Simulations of Human Behavior” published by the Stanford and Google joint team, a virtual town was constructed, and its residents were endowed with ChatGPT capabilities, enabling them to possess memory, communication, and interaction abilities, simulating human behaviors such as cooking, washing, and socializing;
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The Google DeepMind team developed a general AI agent called SIMA, which can follow natural language instructions in a wide range of 3D virtual environments and video games to perform various tasks, such as driving, digging, and shooting; -
A research team from the University of Hong Kong and New York University collaborated on a project called V-IRL, which aims to bridge the perceptual gap between the digital and real worlds, allowing AI agents to interact with the real world in a hybrid environment. -
A research team in China developed an AI-based early screening model for pancreatic cancer called PANDA. This model can utilize AI to amplify and identify subtle lesion features in plain CT images, accurately detecting and diagnosing pancreatic ductal adenocarcinoma and non-pancreatic ductal adenocarcinoma lesions, and can be used for large-scale screening of asymptomatic patient populations. This achievement is expected to improve the early diagnosis rate of pancreatic cancer, providing better treatment outcomes for patients.
However, despite significant progress in AI agents, challenges and risks remain. Among them, data privacy is a critical issue in AI agent research. As AI agents are widely applied in various fields, ensuring user privacy and data security, as well as ensuring that AI agents’ behaviors comply with legal and ethical standards, are urgent issues that need to be addressed.
#04-Future Applications of AI Agents
AI agents can autonomously complete various tasks and exhibit higher levels of personalization across various fields, better meeting heterogeneous human needs.
AI agents can analyze traffic data and real-time road conditions to provide scientific basis for traffic management and planning, enhance traffic efficiency, reduce safety hazards, accelerate emergency responses, alleviate parking pressure, and optimize road resources. Moreover, AI agents can leverage their powerful computing capabilities to process vast amounts of traffic data in real-time, ensuring driving safety and making autonomous driving possible.
AI agents can autonomously interact with healthcare systems and patients, simulating human language and behavior to provide personalized medical services. They can assist doctors in diagnosing diseases, analyzing pathological slices, and formulating treatment plans, thereby improving healthcare efficiency and quality.
With the development of smart home technology, AI agents can leverage their powerful learning capabilities to become “smart housekeepers” for households, helping people manage home devices and improve quality of life.
Source: Intelligent Manufacturing IMS