2025 is referred to by many as the “year of the agent”. As enterprises deepen their reliance on AI Agents, they have evolved from mere auxiliary tools to capable “digital employees” that can independently execute tasks.
So, what exactly is an AI Agent? How should it be applied? What should be noted when using it?
01 What is an AI Agent?
AI Agent is an intelligent entity capable of perceiving its environment, making autonomous decisions, and executing actions. Its core capability architecture includes four key dimensions: perception, planning, action, and memory. Unlike traditional AI systems, an Agent can not only answer questions but also proactively complete a series of complex tasks.
In other words, an AI Agent is a large model with action, planning, and memory capabilities. Models like ChatGPT excel at thinking and generating content, while AI Agents can leverage search engines, booking systems, and data tools to turn ideas into tangible results.
For example,
when you ask Doubao:
“I will be on a business trip to Beijing next week and want to balance work with exploring shops. Do you have any recommendations?”
It can provide multiple plans and routes, helping you strategize and find the best answers.
However, when you continue to ask:
“Can you help me book a suitable hotel based on your recommendations and book round-trip flights?”
Its response can only be that it cannot complete this task.
Because large models can only answer your questions, acting as a “super brain”. They lack “hands”, “feet”, and tools, and thus cannot complete the next step of practical operational tasks.
But an AI Agent can.
It adds hands and feet capable of completing tasks outside the brain of the large model.
It can follow your instructions, conduct its own searches and processing, and directly help you achieve the results you want.
02 How AI Agents Work
Upon receiving instructions, the Agent collects information from the environment and extracts relevant knowledge through the large model.
Then, it utilizes its planning capabilities to plan the response based on the user’s question, determining the tools or knowledge resources to be invoked. At the same time, it processes and analyzes information using logic and algorithms to make decisions.
According to the plan, the various tools and resources invoked by the Agent, such as calculators, search engines, databases, etc., expand functionality and execute specific operations, such as sending messages, executing code, controlling devices, etc., to achieve goals.
During this process, the AI Agent utilizes its powerful memory capabilities to store and retrieve information. Short-term memory helps the Agent remember key information during the current interaction, while long-term memory is used to accumulate knowledge and experience for reference in subsequent tasks, continuously honing and improving its ability to process information.
03 Where is the Agent Particularly Useful?
Everyone is excited about the development of Agents, and many researchers are exploring using Agents for market research.
So, how does the Agent specifically work?
Like other AI large model applications, you can pose a business problem that needs analysis in the dialogue box, and the system will ask three to five questions to clarify your specific objectives.
For example, if you want to study user feedback on a newly launched product, the system will ask you:
“What role are you taking in researching this issue?”
“Do you want to understand performance feedback or user experience feedback?”
“After obtaining this feedback, do you plan to use it for new product development or competitive product research?”
Through these follow-up questions, the system will better understand your needs.
Afterward, the system will organize the previous Q&A content into a series of specific work tasks.
Next, the Agent will conduct real-time searches on social media, and after searching, it can see many posts, including original texts and comments.
Based on this context, it simulates the typical consumer profile of the posting users.
The work they do can significantly shorten the tedious and complex search time. Additionally, based on instructions, they can independently complete part of the work tasks, greatly improving work efficiency.
04 Application Scenarios and Development Prospects of Agents
By 2025, the market size for AI large model applications is expected to reach approximately 32.8 billion yuan, with a compound annual growth rate of 131% from 2022 to 2027. The market’s emphasis on AI applications remains extremely high, but this year, the usage rate of leading large models has declined. As of September 2025, the usage rate of Deep Seek has significantly dropped from 50% at the beginning of the year.Enterprise users are more inclined to procure Agent applications that can directly solve business scenario problems.
Since 2024, the financing amount in the global AI Agent sector has exceeded 66.5 billion yuan. Leading institutions have made substantial investments in numerous Agent projects. Domestically, many investment institutions are actively participating in the investment of AI Agent projects. Some investment institutions not only provide financial support but also continuously focus on technological research and product innovation to help enterprises grow rapidly.
The application scenarios for AI Agents will continue to expand, from office Agents to vertical Agents, and then to broader industry applications. Companies like Future Intelligence have already achieved normalized applications of Agents in industries such as electricity, finance, the internet, and manufacturing.
In the future, as AI Agent technology continues to develop and mature, it may deeply penetrate vertical industry applications, becoming an essential tool for enterprises.
AI Agents will gradually become standard in industries such as electricity. For example, in the electricity sector, they can perceive fluctuations in grid data and autonomously complete fault diagnosis, load allocation, and other operations, reducing the costs and risks of manual inspections;
Medical Scenarios
AI Agents can integrate patient medical records and examination data to provide doctors with preliminary diagnostic suggestions and treatment plan references, and can also automatically follow up on patients’ postoperative recovery and send medication reminders.
Technical Field
AI Agents can achieve full-link automation efficiency in the IT field, covering key aspects such as development, operations, security, and management, solving repetitive labor issues while lowering technical barriers and reducing human errors.
Financial Industry
In the financial industry, in addition to regular public opinion analysis and risk warnings, it can also delve into credit review scenarios, autonomously integrating corporate operational data and credit records, generating review reports and highlighting risk points, significantly shortening the review cycle.
Multi-Agent Collaboration
The capabilities of a single AI Agent are limited, while multi-agent systems will become a trend. Different functional Agents can form a “virtual team” to collaborate and complete complex tasks.
For example, in preparing for a large event, an information-gathering Agent can find venue resources, a budget-calculating Agent can manage costs, a promotional Agent can develop communication plans, and a coordinating Agent can synchronize progress across all aspects, significantly enhancing the execution efficiency of complex tasks.
Human-Machine Collaboration
Human-machine collaboration is becoming mainstream, reshaping our work and life patterns.
AI Agents do not replace humans but form an efficient collaborative model of “human-machine cooperation”. In the future, over 15% of daily work decisions will be autonomously completed by AI Agents, allowing humans to focus more on creative and decision-making core tasks.
For instance, in the creative industry, Agents can handle material collection and initial draft creation, while creators focus on optimizing ideas and enhancing the quality of their work.
05 Issues to Consider When Using Agents
Although many large model manufacturers have launched Agent platforms, they are primarily concentrated in areas such as personal assistants, entertainment, and writing, which have relatively low requirements for reliability and rigor. While AI Agents possess the technical processing capabilities for real market applications, they still face many issues in actual productivity scenarios, requiring extra caution when used.
The main issues stem from their brain—the large model (LLM).
AI Agents use large models as core components to understand user needs, plan tasks, and generate responses. However, large models still exhibit uncertainty, which can lead to a series of potential problems in the use of Agents.
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Incorrect task planning and suggestions
If the large model misinterprets the user’s intent or semantics, it may lead to incorrect plans and outcomes.
In fields requiring high accuracy, such as healthcare, law, and finance, incorrect suggestions and uncertainties can have serious consequences.
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Insufficient reasoning and memory capabilities
AI Agents rely on large models as a foundation, but large models lack robust world model understanding capabilities, resulting in poor reasoning generalization across different industries and scenarios. For example, a risk assessment Agent well-suited for the financial sector may struggle to be directly applied to equipment fault assessment in manufacturing.
Its memory management has shortcomings; complex tasks generate a large amount of action-result memory data, which can easily exceed the context limits of the large model and make it difficult to filter out useful information for decision-making. In complex projects requiring long-term follow-up, memory confusion may lead to omissions or repeated actions in task steps.
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Immature multi-Agent collaboration mechanisms
Complex tasks often require multiple AI Agents with different functions to collaborate, but currently, there is a lack of unified standards for communication protocols and data formats among different Agents, leading to potential information transmission discrepancies; and there is a lack of efficient collaborative scheduling mechanisms, which may result in multiple Agents redundantly executing the same task or shirking responsibilities, affecting overall execution efficiency.
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Security issues, high risk of sensitive data leakage
When invoking multiple external tools to handle tasks, there is a risk of attackers exploiting the situation to leak sensitive information. Attackers may induce the Agent to directly generate content containing trade secrets or personal privacy, causing irreversible information security damage.
Of course, these issues will continue to be optimized and improved through the joint efforts of model developers, tool providers, and application developers.
Perhaps AI Agents will not become perfect “digital partners” overnight,
but the direction of “autonomous intelligence” they represent has already opened a new chapter in human-machine collaboration.
In the future, AI Agents will ultimately be safer and more aligned with human needs.
What we need to do is to embrace this technology with an open attitude,
and work together to promote the intelligent era towards a more sustainable future.
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