From unlimited Tokens to all-staff Agents: MiniMax opens the door for organizational change in AI companies

While most companies are still discussing "how to use AI to improve efficiency," MiniMax has transformed itself into an AI Native company. This is not a marketing rhetoric, but a real organizational restructuring-all employees work with Agents, there is no limit on Token processing, and the traditional "department-team-individual" pyramid is being completely dismantled. This public disclosure in May 2026 allowed the outside world to see clearly for the first time what was happening within the company.

This is not a gradual reform, but a risk with no turning back. Some people are optimistic, some question, and more people are watching. But no matter what, MiniMax's practice provides the entire industry with a living sample: What should AI Native look like?

1. Event/Technical Background

MiniMax disclosed a large amount of information to the public this time, with two core things: the unlimited breakthrough in Token processing capabilities and the agent-based organizational structure of all employees.

Let's first talk about the fact that there is no upper limit for Token. Traditional AI model reasoning has context window limitations. In the early stage, it may have been 4K, and later developed to 32K and 128K. Now, some large models claim to support 1M contexts. But this "support" comes at a cost-the longer the Token, the higher the reasoning cost, the more obvious the delay, and many products will compromise between price and length. MiniMax's technological breakthrough is that they claim to solve the problem of reasoning efficiency in long-context scenarios, allowing ultra-long text to be processed without restrictions in actual business. This is not a simple technical optimization, but an architectural redesign.

Let's talk about all agents. The term is used in the industry, but MiniMax's practice is completely different from what most companies call an "AI assistant." Their employees do not "work with AI tools" but "delegate their work to AI Agents." Each position has a corresponding Agent. This Agent understands all the context of the position and can complete most of the standardization work independently. Human employees have changed from executors to reviewers and decision-makers.

According to qubit reports, the information disclosed this time came from a technical sharing meeting within MiniMax. The meeting demonstrated the company's complete evolution path in organizational structure and engineering practices over the past 18 months. Without hiding anything, he directly told the whole story of the internal system architecture. Behind this transparency is MiniMax's confidence in its technical strength, and may also be paving the way for subsequent financing or commercialization.

2. Analysis of core technology principles

To understand MiniMax's approach, you must first understand two technical concepts: infinite context and Agent architecture.

Implementation principle of infinite context

The core problem faced by traditional Transformer architectures when processing long text is that the time complexity of attention calculation is O(n²). The longer the text, the amount of computation explodes. MiniMax's solution introduces a sparse attention mechanism and a hierarchical processing architecture. Simply put, they no longer do complete attention calculations for the entire context, but instead let the model learn to "selectively pay attention"-only fine processing of the really important information, and processing other parts by compression or retrieval.

The technical details behind this include: a context compression algorithm based on semantic clustering, which can identify and retain key information; a dynamic routing mechanism that allows models to automatically select optimal computing paths at different lengths; and a special training strategy for long contexts that allows Models learn to handle long-distance dependencies.

Technical architecture of all agents

MiniMax's Agent system is not a single point product, but a complete collaboration framework. Each Agent consists of three core components: a planning module (understanding tasks and dismantling steps), an execution module (calling tools and manipulating data), and a memory module (accumulating experience and continuous learning). The combination of these three allows Agents to handle complex multi-step tasks rather than just answering a question and ending it.

Key technology selections include: using Model Context Protocol (MCP) as the communication protocol between Agents; building an enterprise knowledge base based on a vector database to achieve RAG enhancement; and a self-developed workflow orchestration engine that supports conditional branching and parallel processing.

4-5 Key technical points:

• Sparse attention mechanism: Reduce O(n²) complexity to close to O(n), and measured latency is reduced by approximately 60% in a 128K context (according to MiniMax technical documentation)
• Layered compression algorithm: Semantically compresses low-value information, retaining more than 95% of key information (industry estimate)
• MCP protocol support: enables cross-agent tool invocation and status synchronization, compatible with 20+ mainstream AI tool ecosystems
• Continuous learning framework: Agents can update their behavioral patterns based on human feedback, rather than resetting them every time
• Security sandbox isolation: Each Agent operates in a controlled environment to prevent the spread of systemic risks

3. Why is this important?

The importance of this matter is not because of the MiniMax itself, but because it proves a path.

In the past three years, the entire industry has had two mainstream paths for AI implementation: one is "AI empowerment", which embeds AI capabilities into existing business processes, essentially tool upgrades; the other is "AI native", which designs new products from scratch. Typical representatives of product forms are AI code tools such as Cursor and Windsurf. But neither approach answers a fundamental question: What would happen if a company were completely AI at the organizational level?

MiniMax's practice answers this question. Instead of optimizing a link with AI, they make AI the infrastructure for the organization to run. This means that the recruitment logic has changed and no longer requires so many people to do the execution work; the decision-making process has changed, and many judgments can be automatically completed by the Agent; even the organizational boundaries have changed, and the Agent can work 7×24 hours without fatigue and emotion.

The impact of this is far-reaching. If this path works, it means that AI's replacement of human jobs is no longer the disappearance of sporadic jobs, but a systematic reorganization of career structures. It is not that a certain customer service is replaced by AI, but that the entire customer service department is redefined.

More importantly, MiniMax proved that the road is engineering feasible. Their employees are less than 200, but they support an average daily processing capacity of hundreds of millions of Tokens. This people-effectiveness ratio is unimaginable in traditional companies. According to industry estimates, a traditional AI company with the same business volume may require a team of more than 500 people.

4. Industry impact and data support

MiniMax's practice has brought several clear data signals to the industry.

AI Native companies 'efficiency boundaries are being redefined

MiniMax currently employs between 150 and 200 people (according to public information), but its AI platform processes billions of tokens per day. In contrast, the average daily processing capacity of other AI companies of the same size is usually only one-tenth to one-fifth of this figure. The gap in per capita Token processing efficiency has reached more than 10 times.

Agents are changing from assistive tools to core workforce

According to industry research data, by the first quarter of 2026, about 35% of large technology companies around the world have piloted Agent applications internally, but cases of truly realizing "all-agent" are extremely rare. MiniMax practices are at the forefront of the industry by 5%. This means that they don't have many mature plans to refer to, and most of their practices are crossing the river by feeling for the stones.

Long-context capabilities become a differentiated competition point

Currently, the context support capabilities of mainstream large models on the market are distributed as follows: About 60% of models support contexts above 128K, but the actual availability (without significant intelligence reduction and no additional charges) is usually limited to 32K; there are no more than 10 manufacturers who truly realize "insensitive long context"(industry estimate).

Changes in organizational form lag behind technical capabilities

A 2025 McKinsey report shows that although 87% of corporate executives believe that AI will profoundly change organizational forms, less than 5% of companies have actually completed organizational structure adjustments. Technology is ahead, but organizations cannot keep up. This is the core contradiction facing the entire industry. The value of MiniMax is that they provide a complete sample of organizational change, rather than just a Powerpoint vision.

Reaction of the developer community

AI Native related projects on GitHub have grown by more than 300% in the past year (according to GitHub's annual review), but there are only a handful of mature solutions that actually involve organization-level Agent architecture. The community does not lack tools, but methodology. MiniMax's public practice fills this gap.

5. Actual implementation cases

Case 1: Thorough restructuring of a content review team

This is one of the earliest teams within MiniMax to carry out agent-based transformation. The original working model was: a review team of 20 people working in three shifts to process user-generated content on the platform. The pain points of manual review are obvious: inconsistent standards, low night shift efficiency, high training costs for newcomers, and auditors have been exposed to negative content for a long time, and frequent mental health problems.

The transformed architecture is: 3 human employees + 15 special review agents. Each Agent focuses on one content type (text, pictures, video, audio), humans formulate review rules and annotation samples, and the Agent is responsible for execution and continuous learning. The role of human employees has changed from enforcers to rule-makers and exception handlers.

Practical results: The number of audits has increased eightfold, and the misjudgment rate has dropped from 3.2% to 0.7%. But more importantly, the work content of the three human employees has changed from repetitive labor to strategy optimization and system maintenance. According to feedback from the team leader, the current job is "more like training and supervising an AI team than doing its own review."

Case 2: Agile Development Transformation of a Product Team

The agent-based transformation of this team is more representative. The product team of traditional Internet companies usually includes roles such as product managers, interaction designers, R & D engineers, and test engineers. The collaboration chain is long and communication costs are high.

MiniMax's approach is to refactor the workflow around Agents: the product manager's Agent is responsible for requirements analysis and PRD generation; the Design Agent can directly produce high-fidelity prototypes based on PRD; the Code Agent implements core functions based on prototypes and PRD; the Test Agent automatically generates test cases and performs regression testing. The role of human product managers has become an "AI collaboration commander"-defining goals, reviewing outputs, and coordinating the work of different Agents.

One detail is very telling: in the past, it might take 2 - 4 weeks to make a new function from requirement to launch; now the development cycle of core functions has been compressed to 3 - 5 days, most of which is for humans to review and make decisions. Agent execution efficiency far exceeds expectations.

Of course, there are costs. According to team members, this working model requires completely different abilities for human beings-they used to be good at "doing", but now they need to be good at "judgment" and "decision-making." Some product managers who are accustomed to execution are very uncomfortable during the transition period, and some even leave their jobs because of this.

6. Comparison with competing products/alternatives

AI Native organizational practice is currently in the early exploration stage, and different companies have different implementation paths. The following is a comparison of several mainstream solutions:

It can be seen from the comparison that MiniMax's plan is the most radical one. It is not simply adding AI plug-ins to existing processes, but redesigning the way the organization operates. The advantage of this approach is that the ceiling is high, and once it is cleared, the efficiency improvement will be an order of magnitude; the disadvantage is that the risks are also high, requiring strong execution and technical reserves across the organization.

For most companies, my advice is: You can refer to MiniMax's ideas, but you don't have to copy them completely. It is a more secure path to start with an agent-based pilot project in a single department, accumulate experience and then gradually promote it. After all, the success of MiniMax is related to their founding team background, financing scale, and business characteristics. It is not realistic to replicate an identical solution.

7. Technical challenges and limitations

To be honest, MiniMax's solution is not perfect, and there are still many problems at least at this stage.

Agent interpretability is a black hole

When an Agent makes wrong decisions, locating the root cause of the problem is very difficult. It may be that the planning module misunderstood the task, the execution module called the tool in the wrong way, or the information accumulated by the memory module was misleading. What's worse is that these three interact with each other, forming a complex chain of cause and effect. In traditional workflows, an error can be traced back to a specific individual; in Agent architecture, an error may be caused by the "collaboration" of multiple modules, which makes review and optimization extremely complicated.

The boundaries of human-computer collaboration have not yet been clearly understood

MiniMax's practice exposes a fundamental question: What should agents do and what should people do? There is currently no standard answer, and different teams and different businesses have different judgment standards. This leads to an embarrassing situation: some Agents assume too much decision-making power, creating systemic risks; some Agents are excessively restricted, and almost everything needs to be confirmed manually, greatly reducing efficiency improvement.

Agent stability needs to be verified

The output of AI models is random, which is not a big problem in dialogue scenarios, but can be fatal in automated workflows. Deviation in the output of one critical step can cause the entire task to fail, and this failure is often discovered after the fact. MiniMax's current solution is to add manual review nodes, but this is essentially exchanging manpower for stability and does not really solve the problem.

New challenges to data privacy and security

When agents are able to access and manipulate enterprise data autonomously, the risk of data leakage increases exponentially. A maliciously induced Agent may inadvertently leak sensitive information, which is almost impossible under traditional models. MiniMax's security sandbox mechanism limits the scope of damage, but cannot prevent all types of attacks.

The transformation pains of organizational culture

This is the most easily ignored but the most far-reaching challenge. Allowing employees to change from "executors" to "supervisors" may sound like a dimension upgrade, but in fact, it is a dimension reduction for many people-they lose their sense of accomplishment at the execution level, but they may not be able to find new meaning at the supervision level. According to internal feedback, some old employees are resistant to the agent-based transformation and believe that their value has been diluted. If this emotion is not handled properly, it will affect the progress of the overall transformation.

8. Who should pay attention to this matter

Technical Decision Makers (CTO, Technical VP)

If you are responsible for the technology strategy of a technology company, MiniMax's practice provides an important reference sample. It tells you that "AI Native" is not just a product strategy, but may involve a complete rebuilding of organizational structures. The engineering challenges this poses are huge: How to design the Agent system architecture? How to ensure stability? How to deal with the boundaries of human-computer collaboration? There are no standard answers to these questions, but MiniMax's practice at least lets you know where the pitfalls are.

product Manager

The impact of agentization on the way products work is disruptive. The traditional PRD→ design → development → testing process may be completely broken, and product managers need to learn to collaborate with multiple Agents to control the overall output quality. This puts forward new requirements for the ability model: the importance of execution capabilities decreases, and the importance of judgment and overall view increases. If you are making or planning to make AI-related products, this trend is worth laying out in advance.

entrepreneurs

MiniMax's practice has verified a hypothesis: AI Native companies may have a crushing advantage over traditional companies in terms of efficiency. This means that if your competitors are the first to complete the AI Native transformation, you may face great competitive pressure. But conversely, if your team is capable of doing it, it could also be an opportunity to overtake in a corner.

investors

In the past, when looking at AI projects, we mainly looked at model capabilities, product experience, and commercialization potential. One dimension may need to be added now: the degree of organizational AI. A company's organizational efficiency ceiling determines how far it can go. The case of MiniMax shows that it is not enough to have good AI products, but also necessary to have supporting organizational capabilities. If you want to invest in AI tracks, this dimension is worth paying attention to.

ordinary developers

No matter what type of company you work in, agentization affects the way you work. Even if your company doesn't transform organization-level agents, the tools you use every day will become more and more AI-based. Understanding how agents work and collaboration models will allow you to adapt to this change more quickly and find a new position in your team.

9. Prediction of future trends

Based on MiniMax's practice and industry status, I have several clear judgments.

Judgment 1: AI Native will become the standard feature of leading AI companies

It is not an option, but a must-have option. When industry competition enters deep water, mere technological leadership is no longer enough-you need the cooperation of organizational efficiency to turn technological advantages into business advantages. MiniMax has proved that this path can work, and other powerful AI companies will follow suit. It is expected that in the next 18 months, more AI companies will disclose their organization-level Agent practices.

Judgment 2: The post model of man-machine cooperation will be redefined

It is not as simple and crude as "AI replaces humans", but "humans do what humans are good at, and Agents do what Agents are good at." What are humans good at? I think they are: judgment in complex situations, integration and innovation of cross-domain knowledge, communication that requires trust relationships, and tolerance for uncertainty. What are agents good at? Standardized, high-frequency work with clear evaluation standards. This branch union is becoming clearer and clearer.

Judgment 3: AI Infra track will usher in an explosion

The practice of MiniMax exposes a huge need: the engineering infrastructure to support AI Native organizations. The current market maturity is not enough. Agent orchestration, workflow automation, long context processing, human-computer collaboration interfaces... Tools and platforms in these fields will see rapid growth. It is expected to see multiple unicorns running out of this track in the next 2-3 years.

Judgment 4: Resistance to organizational change is more difficult to overcome than technological breakthroughs

This is the biggest inspiration from the MiniMax case. Technical questions have answers, but they just take time. But organizational change involves people's interests, habits and identities, and this is a more complex game. The success of MiniMax is related to the control of the founding team and the company culture. If it were a large company with a traditional enterprise, the same technical solution might not be able to be pushed at all. This reminds everyone who wants to learn MiniMax: technology can be replicated, but organizational capabilities cannot.

X. Recommendations for action

Don't wait, start the pilot now.

No matter where you are, as long as you are in an AI-related industry or position, you should start thinking about the impact of AI Native on yourself. At the technical level, we focus on the technological evolution of long-context processing and Agent architecture; at the organizational level, we evaluate which links in our team's work process are suitable for agent-oriented; at the individual level, we think about how our core competencies can remain competitive under the human-computer collaboration model.

MiniMax's practice is a reference, not a Bible. Their plan has adaptations and costs, and how to use it must be combined with your own actual situation. But one thing is certain: AI Native is not a future trend, but a fact that is happening. It is better to act early than to act late. It is better to cross the river by feeling the stones than to stand on the bank and watch.

The first step in action is simple: Find one of the most frequent and standardized tasks in your team, and try to find a ready-made Agent tool to assist with it. Don't pursue perfection, run first and understand the ability boundaries and collaboration methods of agents in practice. This is more effective than any theoretical study.