From "artificial intelligence" to "true intelligence": How can AI agents break through the last technical barrier?

In the field of artificial intelligence, retrieval augmentation generation (RAG) systems have attracted much attention for their ability to extract relevant information from massive amounts of data and generate intelligent answers. However, AI agents have taken this capability to a whole new level. They are not just tools for acquiring and processing data, but intelligent systems that can think, plan, and take action autonomously. Imagine having an assistant that not only helps you organize documents, but also determines how to handle them, arranges subsequent steps, and flexibly adjusts strategies when the situation changes - this is the future promised by AI agents.

Compared with RAG systems, AI agents are not limited to retrieving information and generating content. They can also coordinate workflows, call external tools, and dynamically adapt to various complex scenarios. Their core is to understand information and decide on the next action, rather than just executing preset instructions. In order to fully understand the revolutionary significance of AI agents, we will explore the methods and driving forces behind them from three key perspectives.

1. RAG Perspective: Unleashing the Full Potential of AI Systems

Modern large language models (LLMs) have amazing capabilities: they can generate text summaries, complete sentences, and even write code. However, if you only regard them as "text generators", it is like owning a Ferrari but not driving it in the garage. Their true potential can only be fully unleashed when they are connected to the outside world - by obtaining real-time data, filtering information and making decisions, AI systems can demonstrate intelligence beyond traditional tools.

It is in this context that the RAG system comes to the fore. It combines the ability to retrieve (find relevant information from external sources such as databases or the Internet) and generate (convert information into useful answers), which is a technological leap. Imagine a super-intelligent librarian who can not only find the book you need, but also write an accurate summary based on your needs. RAG greatly improves the performance of AI in dealing with complex problems by incorporating external context into the answer.

However, the RAG system also has its limitations. It usually relies on preset scripts to run: programmers decide which databases to search, which keywords to search for, and how to generate them. Although powerful, it lacks true autonomy - when faced with unexpected situations, RAG requires human intervention to adjust. AI agents go a step further. They can not only perform tasks, but also "write their own scripts" in action. Specifically, the unique advantages of AI agents include:

• Autonomous reasoning and planning : The AI ​​agent is able to develop an action plan based on the mission objectives and dynamically adjust it based on feedback during execution. For example, when answering a multi-level question, it can decide which part to solve first without human guidance.

• Self-correction : The AI ​​agent can reflect on itself and correct itself when it makes mistakes. For example, if the generated answer is not accurate enough, it will retrieve information or adjust the reasoning path.

• Flexibility beyond scripts : Unlike RAG’s fixed processes, AI agents can cope with uncertainty and autonomously find solutions to problems.

Simply put, RAG is like an "external hard drive" for AI, which enhances its knowledge reserve; while AI agent is like giving AI a "brain" so that it can think and make decisions. In 2025 and beyond, AI agent is becoming the key to unleashing the full potential of AI.

2. Software development perspective: Get rid of the constraints of rigid rules

From a software development perspective, traditional programming relies on strict control logic. Developers must write clear rules for each situation: "If X happens, do Y; if A fails, switch to B." This "if-then" pattern is predictable, but it often appears rigid and inefficient when faced with complex or changing tasks. Take an automation task as an example: you need to define the input (such as a piece of text), the conversion process (such as format adjustment), and the output (such as a file). When new requirements or abnormal situations arise, developers must constantly add new rules, causing the code to become bloated and difficult to maintain.

AI agents have completely overturned this model. Instead of requiring developers to micromanage every detail, they act like a smart colleague and complete tasks autonomously based on goals. Specifically, the methods of AI agents in software development include:

• Dynamic information collection : Instead of hard-coding data sources, the AI ​​agent can find the required information on its own. For example, when generating a report, it can automatically extract the latest data from multiple databases.

• Fuzzy problem handling : Faced with unclear or complex requirements, the AI ​​agent finds solutions through reasoning without having to presuppose all the conditions. For example, when debugging code, it can analyze errors and try to fix them.

• Tool usage and integration : AI agents can call external tools (such as APIs, computing software) to expand their functions. For example, when processing financial data, they can call analytical tools to calculate results and generate visual charts.

• Flexible input and output : Whether it is text, PDF or JSON, AI agents can handle multiple forms of input and output code, lists or structured data as required.

Take automated testing as an example. Traditional methods require developers to manually write test cases and define expected results. AI agents can autonomously generate test cases, execute tests, analyze results, and even automatically fix the code when problems are found. This flexibility and intelligence not only improves development efficiency, but also reduces human errors.

Of course, traditional software still has its value in simple, predictable tasks. But when tasks become chaotic or unpredictable, AI agents are like "free programmers" who can shine with their adaptability and creativity.

3. Real World Example: News Aggregation, Old vs. New Approach

In order to more intuitively demonstrate the advantages of AI agents, we take the news aggregation platform as an example to compare the differences between traditional methods and AI agent methods.

1. Traditional methods

In the traditional way, operating a news aggregation platform requires the following steps:

• Information scraping : Writing scripts to scrape articles from a specific website, relying on fixed URLs and page structures.

• Classification and filtering : Categorize articles based on preset keywords or tags (such as "sports" and "technology").

• Manual maintenance : When the website layout changes or new topics (such as "AI ethics") emerge, the crawling rules and classification logic need to be manually updated.

While this approach works, it’s high maintenance and inflexible. It’s like playing whack-a-mole—as soon as you solve one problem, another pops up. For example, if a news site redesigns, the scraping script won’t work; if users become interested in new topics, the system can’t automatically adapt.

2. AI Agent Method

Now, imagine an AI agent taking over this task and everything changes:

• Intelligent retrieval : The AI ​​agent can autonomously discover new sources of information without pre-setting URLs. For example, it can find popular news sites by analyzing network trends.

• Adaptive classification : Using natural language processing technology, the AI ​​agent can instantly classify articles and even identify unexpected new topics (such as "metaverse").

• Real-time optimization : Based on user behavior and feedback, the AI ​​agent can dynamically adjust content strategies. For example, if the user prefers technology news, it will prioritize relevant content.

• Self-correction : When irrelevant content is captured or classification is incorrect, the AI ​​agent will automatically adjust the algorithm to improve accuracy.

Through AI agents, news aggregation platforms can not only collect and classify information more efficiently, but also provide personalized content recommendations. It is no longer a mechanical tool, but an intelligent partner that can think with you. This approach brings flexibility and intelligence that traditional methods cannot achieve.

Why AI Agents Are Important

AI agents are not just an upgrade to the RAG system, but a qualitative change from passive execution to active decision-making. They demonstrate an unprecedented level of intelligence through autonomous reasoning, tool use, and dynamic adaptation. Whether it is making software development easier or transforming news aggregation into an intelligent planning system, AI agents are redefining the way we interact with technology.

AI agents will become indispensable. They can automate complex tasks (such as scientific research and medical diagnosis), enhance human capabilities (such as improving productivity as intelligent assistants), and even promote innovation (discovering new solutions through autonomous exploration). They are not just tools, but partners who can work with humans to solve problems we are not even aware of.

V. Conclusion

AI agents have surpassed the limitations of RAG systems through autonomous planning, tool integration, and self-correction, and have demonstrated outstanding efficiency and intelligence in scenarios such as software development and news aggregation. They represent the next frontier of artificial intelligence, not only improving the practicality of the technology, but also pointing the way for future development. As AI agent technology matures, they will become an important force in promoting social progress and bring a new intelligent experience to mankind.