Pioneering the exploration of future terminals for intelligent agents, STEPX Neo unlocks a new form of human-machine symbiosis.

Personal Agent is becoming the new main theme of the AI industry.

In overseas markets, Google launched Gemini Spark that can work continuously in the background, Meta released Muse, a personal intelligent agent for ordinary users, and OpenAI also entered the competition with its resident intelligent agent Dots.

Capital has also begun to heavily bet on this track: personal AI assistant company Town completed a US$55 million Series A financing, Hark, which focuses on the general personal AI entry, obtained US$700 million in financing, and AI assistant Instinct was also exposed to have raised US$250 million at a valuation of US$2.5 billion.

The simultaneous occurrence of intensive entry of major manufacturers and high-value financing means that the industry is no longer just competing for chat tools, but for the next-generation personal entry that can understand users in the long term, connect personal data and complete tasks on behalf of users.

Actions in the domestic market are equally intensive. On October 13, Step Terminal officially released its first large model native intelligent agent phone STEPX Neo.

This is not only Step Terminal's response to the "100-day appointment" on July 13, but also a long-brewed product answer under the boom of personal intelligent agents. At the same time, the second-generation Doubao phone and AI phones from manufacturers such as Huawei, Xiaomi, OPPO and VIVO are also making concentrated appearances. As a company that started with large models, Step Terminal's understanding of intelligent agent phones and its way of building terminals from scratch are worthy of separate discussion.

Careful observation shows that the starting point of most AI phones on the market is still an existing phone. Chips, systems and interactions are already finalized, AI is added item by item in the form of functions such as search, image editing, Q&A or cross-application operation, and relevant capabilities are also scattered in different entries. The reality is that the concept of AI phones has been popular for several years, but ordinary users' perception of it is still limited.

STEPX Neo chooses to define the terminal in reverse starting from Agent — according to the perception, memory, permissions and models that the intelligent agent requires, software and hardware are redesigned around these demands, and finally form a "software-hardware-model" closed loop covering models, systems and terminals.

To understand it with a simple metaphor: most AI phones are installing smart furniture in an old house, while Step Terminal tries to rebuild a brand new house completely according to the lifestyle of the intelligent agent.

So, on STEPX Neo, how does the "native school" Step Terminal make a mobile phone exactly?

01

In the era of intelligent agents, the right to define the entry of personal terminals is being reopened

It is no accident that Step Terminal cuts into the terminal field starting from large models. When intelligent agents gradually change the way of human-computer collaboration, it naturally brings new opportunities for personal terminals.

In the past two years, Coding Agent first achieved commercial success, and the reason is not complicated — the code world has rare certainty: the rules are relatively clear, the goals can be described clearly, and the execution results are easy to verify. This also promotes AI to further move from providing answers and suggestions to understanding goals, planning steps and completing tasks.

However, the difficulty increases sharply when moving from Coding Agent to the deep water area of Personal Agent.

Personal Agent does not serve structured code, but the continuous, fuzzy and highly personalized daily life of billions of consumers. Moreover, users cannot actively tell the intelligent agent their location, work and rest schedule, preferences and status every day.

This means that Personal Agent cannot only call tools after receiving instructions, but also gradually understand a person in long-term use and judge what users may need in specific scenarios. From "helping users complete a task" to "understanding what users need at the moment", there is a whole chain including scenario perception, long-term memory, intention judgment, task planning and service execution.

This chain is also reshaping the role of personal terminals.

In the past, mobile phones were mainly containers for applications and content, and users needed to actively open apps, find functions and complete operations; in the era of intelligent agents, terminals also need to understand human intentions, schedule different services and deliver results. This means that the interactive entry may gradually shift from individual Apps to Agents that can understand the full needs of users.

Mobile phones have thus re-emerged as a key carrier for Personal Agent competition. In contrast, cloud-based Agents are good at processing web pages, documents and online services, but it is difficult for them to continuously perceive the real environment a person is in. Mobile phones are carried around for a long time, connected to schedules, locations, communications, images and life services, making them the computing device closest to individuals with the most complete information dimensions.

But the problem is that existing mobile phone systems are mainly built around Apps and users' active operations. When the interactive subject shifts from App to Agent, the system also needs to undertake tasks such as long-term memory, model scheduling, tool calling, permission control and cross-service execution. Simply adding an AI assistant to the original system may not be enough to support this change.

Step Terminal entered the AI hardware sector precisely because it saw various pain points in the industry — mobile phones have rich real scenarios, but few of them truly build underlying capabilities around Agent; models have intelligence, but lack continuous perception of the real world and long-term entries to reach users.

STEPX Neo tries to bridge the gap between the two, and also wants to make the hardware outline in the era of intelligent agents more specific.

02

Building a large model native intelligent agent phone, Step Terminal takes an unconventional path

At present, the exploration of AI terminals is still scattered and lacks unified standards. Traditional mobile phone manufacturers add AI search, image processing and cross-application execution capabilities to operating systems; Internet companies extend to terminals relying on cloud models and service ecosystems; hardware players such as glasses and earphones cut in from vision, voice and portable interaction.

If we observe not by the identity of players, but by the way AI enters the terminal, these explorations can be roughly divided into two routes:

The first one is the integrated route. It starts from the existing mobile phones, operating systems, Apps or hardware forms, and integrates the capabilities of large models and Agents into the original product system.

Such players can use the mature user scale, system capabilities and service ecosystem to launch products faster. However, AI needs to adapt to the existing architecture, and relevant capabilities are easily scattered in different applications and entries, eventually staying at the level of function superposition or partial transformation.

The second one is the native route. It does not determine the terminal form first, but first defines what perception, memory, reasoning and execution capabilities Personal Agent needs, and then designs models, systems and hardware in reverse.

Step Terminal has chosen this more demanding path.

Personal intelligent agents are not faced with one-off Q&A. They need to continuously perceive the environment, form and call long-term memory, schedule different models according to the complexity of tasks, and manage tools, permissions and execution processes at the same time. It is difficult to form a complete experience by upgrading any single link alone.

This is also the reason why Step Terminal emphasizes the trinity of "software-hardware-model": models are responsible for understanding and decision-making, software is responsible for organization and scheduling, and hardware is responsible for perception and carrying. The hardware obtains scenario information, the system connects the information with personal memory, tools and permissions, the model identifies intentions and plans tasks, and the system then calls end-side capabilities or external services to complete the execution.

Specifically, at the model layer, STEPX Neo has built a model matrix composed of Pro, Flash and Edge — complex tasks are handed over to the cloud Pro model, the Flash model balances effect and response speed, and the end-side Edge model handles immediate response and privacy-sensitive tasks. The system completes scheduling through end-cloud collaborative routing, prioritizes tasks that can be completed locally on the end side, and hands over tasks that require deep reasoning to the cloud.

If the model determines whether it can understand and judge, what Step AOS solves is how these capabilities can be organized.

Traditional AI assistants usually exist in the form of Apps or system functions, providing help around current conversations and single tasks. Once the interaction ends, the task context is easily interrupted. Personal intelligent agents need to cross different applications and real scenarios, and continuously manage memory, tools, permissions and tasks.

Step AOS tries to upgrade Agent from an isolated application to a system-level capability, allowing the model to call terminal functions and external services within clear permission boundaries. As a result, AI is no longer just a tool waiting for users to open, but begins to become the organizer of different tasks.

This system-level capability is first reflected in the "dual entry" design of Step AOS. After turning on STEPX Neo, you can find a rare design with two entries — one side is the familiar mobile phone desktop for users, and the other side is the exclusive system-level space for Step Amoo. When the traditional App world and the intelligent agent entry exist at the same time, the process of users using mobile phones also becomes the process for Step Amoo to gradually understand users.

In addition, Step AOS also decomposes multiple terminal capabilities into atomic capabilities that can be orchestrated by Agent, and is compatible with multiple connection protocols such as A2A, MCP, Skill, API and CLI. This allows Step Amoo to not only call the functions of the mobile phone itself, but also connect external applications and professional intelligent agents to match corresponding services according to user intentions.

Moreover, Step Terminal has also launched Step Studio, a native development tool for AI terminals, through which users can create AI applications, games or new Agents in natural language. Behind this concept is that mobile phones are no longer just running pre-designed applications, but also allowing users to combine and create capabilities according to their own needs.

According to the introduction, long-term memory is another key design behind STEPX Neo.

Step Terminal has designed a memory system of "dual domains, six layers, memory, organization and recollection". The "user domain" precipitates fact memory, episodic memory and user portrait, and the "intelligent agent domain" accumulates vertical knowledge, cognition and personality, as well as methods and experience for executing tasks. After the information is recorded, it will be sorted, deduplicated, fused and updated, and finally called in appropriate scenarios.

The key of this memory system is not to make the Agent remember more, but to make it recall the truly useful information at the right time. Otherwise, no amount of personal data will naturally be transformed into understanding, but will increase the risk of misjudgment and information abuse.

After actually getting STEPX Neo, what impresses me the most is not an isolated AI function, but that the mobile intelligent agent begins to try to take over a complete task chain.

For example, calling a taxi. In the past, users needed to open the App, confirm the location, enter the destination, select the vehicle type, and then wait for the vehicle to accept the order; on STEPX Neo, users are more likely to directly express "where I want to go", and the rest of the information confirmation and service call are completed by the Agent.

Another scenario that impresses me deeply is that STEPX Neo can access the Skill of WeChat Reading. It is not that the terminal Agent rebuilds a reading application, but converts the existing content and service capabilities of WeChat Reading into Skills that the Agent can understand and call. After access, my mobile phone can tell me what my reading taste is based on my bookshelf, and based on my knowledge framework, it suggests what books I should supplement to read.

These two scenarios also make it easier for users to understand what Step Terminal calls "native". It does not simply put a chat assistant into the mobile phone, but tries to make Agent a new layer that organizes system capabilities and external services.

At the press conference, Step Terminal stated that in the era of intelligent agents, AI is no longer the added value of smart phones, but the product value itself. Based on this judgment, the STEPX Neo first version adopts the full-stack pricing model of "software-hardware-model", which integrates and markets the Step series models, Step AOS system, Step Studio tools and STEPX Neo mobile phone hardware, enabling the intelligent agent terminal to enter an evolutionary mode of rapid iteration.

In the future, whether this chain from understanding users to calling services and finally completing tasks can run stably is the real watershed between the native route and the function-integrated route.

03

The difficulty of making intelligent agent phones lies beyond the mobile phone itself

Even if the integration of models, software and hardware is solved, intelligent agent phones still face two thresholds outside the mobile phone: internally, how to gain user trust; externally, how to build a service ecosystem.

When Agent starts to read personal memory, judge implicit needs, and call services or even complete transactions on behalf of users, whether users are willing to authorize is more important than whether AI functions are rich. In the past, if a chat answer was wrong, users could ask again; if a real-world task was executed incorrectly, it may directly lead to order, payment or privacy risks.

In response to this problem, Step Terminal proposes a security framework of "trustworthy, visible, controllable and reversible". The identity and authorization of Agent need to be verifiable, the execution process should be queryable and traceable, the task scope, quota and time limit need to have clear boundaries, and revocable operations should support rollback.

The security framework answers the question of whether users dare to hand over tasks, and ecological cooperation solves the problem of how many services the Agent can call after receiving tasks.

In the past, one of the biggest controversies about AI mobile phones was the ecosystem. Once an intelligent agent reads information across applications, calls services or completes transactions, it will touch the account system, data boundary, risk control rules and traffic entry of the platform.

The industry previously tried to complete cross-application operations through screen recognition and simulated clicks. This route can cover more Apps in the short term, but once the page is revised, the execution chain may fail. In the face of high-risk links such as payment, transfer and private data, it is also difficult for the platform to judge whether the operation comes from the user himself or an external intelligent agent.

Therefore, the ecological problem of AI mobile phones cannot be solved by adapting to a few more applications. Different participants need to re-agree on the identity, authorization method, data scope and responsibility boundary of the intelligent agent.

The solution given by Step Terminal is "H (Human) to A (Personal Agent) to A (Service Agent)": the system-level Personal Agent understands the user's intention, and then hands over the task to the service Agent with professional capabilities. STEPX Neo is responsible for perception, memory, intention recognition and task orchestration, partners open corresponding services through interfaces, and key links such as