Summary of Key Points
This discussion focuses on AI Agent technology and highlights three main points:
1. The competitive focus in the AI industry has shifted from the models themselves to the “harnesses” (management systems that surround the models).
2. MiniMax’s Agent Team architecture, which divides tasks among a Leader, Worker, and Verifier, has addressed challenges in long-term projects and become an innovative direction of interest in the industry.
3. Agent technology will profoundly change future work methods and may even reshape the job market, leading to new considerations about the relationship between humans and AI.
Detailed Analysis
1. The “Battlefield” of Agent Technology Has Changed: From “Model Intelligence” to “Harness Management”
Previously, AI companies competed on how intelligent their models were—whichever could write code faster or perform tasks more accurately. However, the capabilities of different models (such as GPT, Claude, and MiniMax) have now become quite similar. The real differentiator lies in the “harnesses” that surround these models.
What is a harness? It’s like the casing around a smartphone: the model is the hardware, while the harness consists of systems, applications, and protective layers that manage permissions, handle errors, and control context (for example, preventing the model from retaining unnecessary information). In the code leaked by Claude, only 1.6% of the code was related to the model’s decision-making; the remaining 98.4% was part of the harness. Now, the industry is competing on who can create more flexible and problem-solving harnesses.
2. MiniMax’s Agent Team: Like a “Mini AI Company” Working for You
MiniMax’s Agent Team divides AI into three roles:
- Leader: Responsible for assigning tasks and planning.
- Worker: Executes specific tasks (such as writing code or editing videos).
- Verifier: Checks the Worker’s results and identifies any errors.
The clever part is that these roles have isolated information flows—if a Worker makes a mistake, it doesn’t affect the others. For example, you can assign a complex task (like writing a market report) before going to bed, set clear criteria (e.g., using the latest data and maintaining logical clarity), and wake up with a completed product. You can also interrupt the process at any time; if you decide to add a competitor analysis, the Leader will immediately assign it to a Worker, just like adding a new task to a team on the spot.
3. Industry Competition: Consensus Quickly Forms, but Details Determine Success
Agents have become a hot topic in the industry. DeepSeek is hiring for Agent development, and Claude Code has also adopted a similar quality-control approach. However, different companies have different approaches:
- Anthropic: Doesn’t trust models and imposes many restrictions to prevent cheating.
- OpenAI: Uses a minimalist framework, relying on the models’ ability to follow instructions strictly (e.g., GPT).
- MiniMax: Takes a middle path—believes in the models but adds reasonable constraints (e.g., grants them permissions while using harnesses to control risks).
Consensus is forming quickly (for example, the multi-Agent concept became the industry standard within a month), but details are crucial for success. For instance, MiniMax’s Agent Team allows users to intervene and adjust tasks at any time, whereas Claude’s dynamic workflow is one-time and cannot be changed mid-process. The company that masters these details will attract and retain users.
4. The Impact of Agents on Work
Agents may replace many repetitive, low-level jobs. For example, video editing: some companies currently use college students to edit videos for free, but in the future, agents might be more cost-effective. If agents become more efficient than humans, companies might stop hiring entry-level employees, requiring new hires to pay to use these tools (e.g., by investing in training with agents before earning a salary).
On the positive side, humans can be freed from repetitive tasks and focus on more creative work. MiniMax’s employees, for example, use agents to perform their jobs while focusing on higher-level thinking.
5. The Future of Agents: Predicting Life and Responding to Unexpected Events
With unlimited computing power, one could create a “life avatar” by inputting personal information, experiences, and context into an AI model. This avatar could provide additional options when making decisions (e.g., whether to switch jobs or take the civil service exam). More importantly, it could help with unexpected events (such as illness or unemployment) by preparing in advance. This isn’t about controlling life but about offering more possibilities and focusing on what’s truly important (family, interests).
Conclusion
Agent technology is rapidly changing the AI industry and the way people work. The current focus is on developing effective “harnesses,” while the future challenge is finding ways to collaborate better between humans and AI. For individuals, instead of worrying about being replaced, it’s better to learn how to use agents to enhance their capabilities. After all, AI is a tool that, when used wisely, can make life easier.