A brief analysis of Ammo white paper: From Vector primitives to multimodal Agent ecology

I took some time and carefully read Ammo's new white paper and felt it a lot. Here are some inspirations:

1. The market's pursuit of AI Agent is essentially that it is not satisfied with AI but is just a query tool for Copilot mode. Users ask what AI answers accordingly, but should be more like the Buddy mode companion growth model, which can understand, think and actively create value and push it to people. This is the key to the AI ​​Agent being brought to a narrative level;

2. The traditional AI monolithic model of web2 started with the focus on "tool-based pragmatism". It is easy to form data source islands in multimodal collaboration, and it is difficult to make a smart breakthrough in the true sense. Although web3 proposed the individual autonomous ideology of AI Agent, it is still far from achieving the goal. AI's autonomous decision-making is far more complicated than imagined. Only by allowing AI to assist in automated learning and path recommendations can people enhance the "symbiotic model" of AI independent learning through feedback can they truly become the dominant direction of AI Agent in the next sense;

3. AMMO defines an abstract space called MetaSpace, allowing all data surrounding the AI ​​Agent to be allocated in the space in the form of a Vector vector. Just like the blockchain initially defines Hash, so that all the protocols and application forms will be the same as those on-chain. This form of starting with Vector can not only serve web3, but also a framework standard suitable for web2 multimodality. Combined with the MAS multimodal collaboration system above it, it can turn the current "think tank" orientation of AI in the academic direction into a "practical" orientation towards practical application scenarios such as work, games, and education;

4. How to understand it in a popular way? We regard MetaSpace as a large shopping center. Each functional layer belongs to a SubSpace. Each area has a different knowledge base. The Buddies system is an intelligent shopping guide system. As a professional shopping guide, Goal Buddies selects some high-quality products for you to recommend; while User Buddies is more like a personal assistant and can provide customized solutions based on your consumption habits and budget; AiPP collects feedback suggestions like the head service desk to improve service quality;

Overall, the AI ​​Agent must be enabled to operate through essential components such as MetaSpace+Buddies+AiPP human-computer feedback system, so as to truly accelerate the mass production and practical implementation of AI Agent;

5. The white paper shows more about an off-chain AI Agent multimodal collaboration framework and engineering implementation ideas. Some definition standards on the combination chain, including ID identity system, Memory memory system, Character feature system, Context context management, Oracle oracle system and other component definitions, still need to be further overcome and explored (I often mentioned the "chained" general standard framework);

above.

It should be said that this is the most emotional and pragmatic project that has seen the implementation of macro architecture, application and engineering implementation ideas in recent times, but after reading the above, everyone may feel confused and abstract. That's right, the path of AI Agent to be truly popularized and applied on a large scale is longer than expected, but more and more excellent teams are indeed coming in, and some innovative solutions and ideas are also brewing. The market is waiting for the birth of an innovative "singularity".