Search Paradigm Revolution: Consensus on Nano AI and Google's "Super Search Agent"
In 2025, "Agentization" is no longer just empty talk but a real "typhoon" that has arrived.
AI applications in various industries are evolving into agents with both "intention understanding" and "task execution" capabilities. Among them, search engines, as the most powerful traffic entry in the Internet era and the most familiar application to the public, should also be the first application scenario to complete "Agentization."
While traditional search engines are still filtering information fragments, search agents can actively identify users' search intentions and integrate massive data into professional-level deliverables: a travel guide, a market analysis report, or a shopping decision. Search is no longer just a carrier of information but a tangible productivity tool.
The subversion and reconstruction of the traditional keyword retrieval model by agents have also turned the concept of "Super Search" into a reality.
01
In the era of AI Search 3.0, what is "Super Search"?
Search engines have long been in a state of being old and ineffective, leaving others helpless. In the era of large models, the potential of AI search is obvious. Established giants will face real risks if they remain unchanged because ambitious challengers, well - planned and driving a wave of change, are eager to seize power by reinventing search in the new era.
There are three distinct product boundaries in AI search. In the era of AI Search 1.0, web page ranking was still retained, and AI Overview was added. Bing, Google, and Baidu all "followed the trend" in this way. Large models are like an intelligent "filter" for traditional search, summarizing the essence, achieving better information aggregation, and piecing together scattered knowledge puzzles. However, the essence is still aggregation and summarization.
When the technology entered the era of AI Search 2.0, search engines truly transformed into "Answer Engines." Searching was for answers, and answers became the primary goal. The web pages retrieved by AI Search 2.0 entered a "hidden state" and became information annotations for certain information blocks of the answer. Many AI - native search engines emerged during this period, and Perplexity is one of the outstanding ones, with its valuation skyrocketing. It competes in terms of the quality and hallucination rate of AI Overview answers, which requires a lot of meticulous and extensive efforts and is not easy.
However, during this period, users did not perceive a significant difference between the experience of AI search and directly interacting with large models. This is because the essence of AI Search 2.0 is still cognitive enhancement, and its ability to judge user intentions and disassemble instructions is relatively shallow.
However, as users use AI more widely and deeply, going beyond the "question - answer" boundary, they need a task engine that can automatically output results with simple input rather than just an answer engine.
For example, when people input "Optimize the company's financial report," they probably expect a directly usable professional report rather than just a tutorial. Such task - oriented requirements are basically impossible to meet in the era of AI Search 2.0.
In 2025, in the era of AI Search 3.0, search agents have finally rewritten the rules of the game, making the "Task Engine," that is, what we call Super Search, possible.
While AI Search 2.0 may still be optimizing answers, Super Search has built a closed - loop from intention input, automatic execution to result delivery. It no longer just answers what the world is but directly helps users change the world.
In the early stage of large - scale application of agents, Super Search currently needs to meet at least the following five capabilities:
First, it has a built - in agent task planning system, which is the key for AI search to solve complex requirements. It avoids users having to manually disassemble tasks and realizes "search as execution."
Second, it supports multi - model collaboration, enhancing the professionalism and accuracy of output content through the complementary advantages of different models.
Third, it has "high - dimensional" information cognitive ability, including supporting MCP tools to achieve in - depth crawling of cross - website and multi - modal data. It can break through the information barriers of platforms and expand the boundaries of search.
Fourth, it supports multi - modal output, meeting diverse scenarios and improving information understanding efficiency.
Fifth, it can combine users' accumulated knowledge bases and memory data to achieve a more in - depth personalized search experience.
Currently, mainstream AI search engines at home and abroad are still in the "pioneering stage," but there is a common trust in the upper limit of agent capabilities.
Among them, Nano AI Super Search and Google's newly released AI Mode are relatively balanced, covering all five capabilities. New Bing's conversational search is strong in basic experience but lacks in in - depth execution aspects such as multi - modal output. The same goes for Perplexity. As an answer engine, it is good at analyzing questions but not at helping users complete tasks.
02
Search engines will inevitably become super agents
The goal of "Super Search" is to deliver high - quality solutions when the user's search threshold is reduced to almost zero. Search engines will also transform from traffic and advertising entry points in the past to productivity entry points for various industries.
On closer inspection, Google's release of AI Mode at this year's I/O Conference is actually a step towards search agents.
The core breakthrough of AI Mode lies in the goal - driven framework, which turns search results from information retrieval into deliverable executable solutions. Google's "ultimate goal" is to transform the search engine into a task automation center. This coincides with the development direction of Nano AI. Google clearly realizes that search engines will inevitably become super agents.
However, the technical barriers for search agents are very high, requiring years of independent search infrastructure capabilities. Even though the highly - anticipated AI - native search engine Perplexity is eager to compete, Google still has the power to define the future of search engines.
The situation in China is similar. Many new search services have emerged in the AI era. Although the threshold for product forms is not high, most products without a solid foundation in search technology mainly rely on accessing and invoking third - party APIs and cannot achieve a closed - loop of core search functions, resulting in uneven output results.
Globally, there are only a few manufacturers that have been refining search engines for the past two decades and have accumulated experience in browsers and complete clients. The competition is not as open as it seems. Search engines such as Google, Bing, and 360, which have been deeply involved for many years, are like energy giants with their own mines, possessing both technology, energy, and infrastructure. For example, the full - stack capabilities from web page crawling, index cleaning to intention recognition are significantly different from AI search engines "assembled" entirely by third - party API calls.
Counter - intuitively, Nano AI Search, which originated from 360, entered the stage of search agents earlier than Google: in early 2025, Nano AI Search had already implemented many pre - functions of Super Search.
In the AI product list in April 2025, Nano AI Search ranked first in the Chinese market of AI search engines on the website list and second globally. In terms of monthly access data, Nano AI Search also far outperformed other domestic AI search products.
Globally, Nano AI also performs outstandingly. Its monthly visits reached 246 million in September last year and nearly 310 million by November, more than three times that of Perplexity AI Search in the same period, making it one of the most visited AI - native search engines globally.
Google and Nano AI, the world's largest search engine and a leading domestic AI search product, have both placed their bets on agents. Backed by the domestic market, Nano AI actually dares to go one step further compared to Google's AI Mode:
First, it breaks through the APP islands that were previously impossible to search, using technology to reunite fragmented information and achieve "full - domain retrieval." Through a series of self - developed MCP tools, its past accumulation in traditional search technology and browser technology, combined with the multi - modal understanding and "high - dimensional" information cognitive ability of large models, Nano AI's solutions break through the "information barriers" ahead of others, demonstrating a deliverable - level standard.
Second, Nano AI emphasizes "search thinking," that is, search quotient, enabling search agents to improve the circular reasoning thinking chain through thinking and dynamic correction, similar to human beings, and conduct heuristic search, thus achieving adaptive intelligent task planning.
The evolution of search quotient allows the search process to actively think like a reasoning - based LLM, with a "thinking chain" in search, which brings a generational improvement in search efficiency and quality. The built - in super agent in Nano AI gives unprecedented "freedom" to search terms. Whether your question is long or short, complex or simple, vague or clear, it can identify the intention, plan corresponding steps, and ultimately solve complex problems.
Nano AI encourages users to "state their purpose directly." Users' search terms can be short and free. When faced with vague or ambiguous questions, Nano AI will guide users to clarify the questions through intelligent counter - questions to confirm search needs. When users' search terms are very long, Nano AI will also disassemble semantics, reason and summarize information, and accurately locate users' needs after active thinking.
03
In the future, the ability to "get things done" will be tested
When search engines evolve from being information - result - driven to a neural center based on digital productivity, the exploration of the "new world" will no longer be about simply presenting search results but about scenarios and ecosystems.
The "search as execution" feature of Super Search already has many practical cases.
For example, in the field of medical research, Nano AI's in - depth research function is particularly prominent.
Users input the question: "I heard that early - stage ALS is easily misdiagnosed as other diseases. What are the early symptoms of ALS? What common problems can it be misdiagnosed as? How to identify and prevent it? How should families take care of ALS patients?"
Nano AI will automatically disassemble the question, call MCP tools, and finally output a PDF report and a visual web page.
Compared with medical questions with reliable sources and data, some open - ended questions involve more complex scenarios and require higher - level AI capabilities in simulating human thinking and heuristic innovation. For example, in response to "brainstorming" - type questions about urban transformation without fixed answers, Nano AI has also delivered professional - level in - depth research reports, thinking and verifying while searching.
It is not difficult to find from these cases that large models are like the brain, and agents in each scenario are like the limbs and joints. Nano AI embraced the "open - ended approach" early on. Whether it is the multi - model collaboration with more than 80 built - in large models, multi - modal delivery, or the integration of more than 100 MCP tools, the purpose is not to build commercial barriers but to help users get things done.
Mary Meeker, the "Queen of the Internet," emphasized the rise of Chinese AI in her report "Trends - Artificial Intelligence," using "fierce competition, the open - source wave, and the rise of Chinese forces" as keywords. Among the top Chinese AI applications she listed, Nano AI ranks fourth, almost on par with Kimi, surpassing Tencent Yuanbao and Baidu Wenxin Yiyan, and is the highest - ranked search engine.
Nano AI is trying to define "search as execution" globally and spread this concept to various industries.
At the just - concluded 360 press conference, 360 released the "Super Search Agent" - Nano AI Super Search Agent. After users submit their needs, the agent can understand users' intentions, break through the "information barriers" of platforms, call more complex tools, and deliver answers end - to - end. According to 360's introduction, the core capabilities of Nano AI Super Search Agent are as follows:
1. Users only need to simply describe their needs, and the agent can automatically find solutions. It can also ask intelligent counter - questions according to different problems, allowing users to directly confirm their needs by selection;
2. While disassembling complex tasks step by step, the agent can break through the "information barriers" between platforms through in - depth search capabilities, achieving cross - platform search and facilitating users' decision - making;
3. After identifying users' questions, the agent can think while searching, verify while autonomously planning tasks, and improve the professionalism of answers;
4. It integrates four "automatics": automatic task decomposition, automatic task planning, automatic tool calling, and automatic execution, solving problems end - to - end;
5. It can search for authoritative information sources across domains and output work documents in multi - modal and