A Brief Analysis of AMMO White Paper: AI Agent Narrative Needs Upgrading

Author: Haotian

I took a moment to carefully read the new white paper of @Ammo_AI, and I was very impressed. Here are some of the inspirations:

1）The market's pursuit of AI Agent essentially lies in not being satisfied with AI as just a Copilot mode query tool, where the user asks and AI responds, but rather should be more like a Buddy mode accompanying growth model, capable of understanding, thinking, actively creating value, and pushing it to people. This is the key to elevating AI Agent to a narrative height;

2. The traditional web2 AI monolithic model started with the main theme of 'tool-oriented pragmatism', which easily forms data source islands in multimodal collaboration, making it difficult to achieve a real breakthrough in intelligence. Although web3 proposes the ideological autonomy of AI Agents, it is still far from achieving the goal. The autonomous decision-making of AI is far more complex than imagined. It is only through the 'symbiosis mode' of AI assisting in automated learning and path recommendation, and humans enhancing AI's autonomous learning through feedback, that can truly become the dominant direction of the next AI Agent.

3）AMMO defines an abstract space called MetaSpace, allowing all data around the AI Agent to be deployed in the space in the form of vectors, just as the initial definition of Hash by blockchain enabled all subsequent on-chain protocols and application forms. This vector-based form of initiation can not only serve web3, but also serve as a framework standard suitable for web2 multimodal, in conjunction with the MAS multimodal collaboration system on top of it, it can transform the current academic orientation of AI into a practical orientation towards practical application scenarios such as work, games, and education.

4. How to understand it in simple terms? We regard MetaSpace as a large shopping center, with each functional layer belonging to a SubSpace. Each area has different knowledge bases, and the Buddies system is an intelligent shopping guide system. Goal Buddies, as professional guides, select high-quality products to recommend to you; User Buddies are more like personal assistants, providing customized solutions based on your consumption habits and budget; AiPP is like a general service counter, collecting feedback and suggestions to improve service quality;

Overall, to make AI Agent operate through essential components such as MetaSpace+Buddies+AiPP human-computer feedback system, etc., truly accelerating the mass production and practical landing of AI Agent;

5）The white paper further demonstrates a multi-modal collaboration framework and engineering implementation approach for chain-based AI Agents, some of which define standards on-chain, including ID identity system, Memory memory system, Character feature system, Context context management, Oracle oracle system, and other component definitions that need further exploration (the general standard framework for 'chaining' that I often mentioned before).

Above.

It should be said that this is one of the most emotional and pragmatic projects I have seen in terms of macro architecture, application landing, and engineering implementation in recent times, but after reading the above, everyone may feel puzzled by the abstract sense. Yes, the path to the widespread popularity and application of AI Agents is longer than imagined, but indeed more and more excellent teams are joining in, some innovative solutions and ideas are also brewing, and the market is waiting for the birth of an innovative "singularity."