Single AI Agent vs. Multi-Agent Systems Currently, the majority of AI Agents in the Web3 ecosystem are primarily conversational language models. These models excel in Natural Language Processing (NLP) tasks, but in essence, they merely provide a direct yet narrow form of human-machine interaction, rather than acting as truly autonomous Agents. A key distinction is that traditional conversational AI is essentially a “talking tool”, while a true AI Agent can autonomously execute tasks, perceive its environment, and dynamically optimize decisions in complex scenarios. To illustrate the significance of AI Agents and the necessity of Multi-Agent Systems (MAS), let’s use urban mobility as an example. Traditional AI vs. Single AI Agent —— Imagine you need to travel from Columbia University in New York to Madison Square Garden: • A traditional conversational AI (e.g., ChatGPT) might respond: “You can take subway Line 1 or a taxi, with an estimated travel time of 31 minutes.” (It cannot even provide a route map.) • A Single AI Agent, however, functions like a smart driver: • It not only plans the optimal route but also autonomously drives the vehicle, recognizing traffic lights, avoiding congestion, and making real-time adjustments. • As a user, you don’t need to micromanage the Agent’s decisions—it automatically selects the best route, handles navigation, and ensures safety. • Additionally, this AI Agent continuously learns and optimizes itself—after multiple trips through Manhattan, it refines its navigation strategy for different times of the day. This type of task execution falls under a Single Agent system, where one AI Agent independently provides a complete service to the user. Multi-Agent Systems:——Solving Complex Tasks While Single AI Agents can efficiently handle simple tasks, larger-scale and more complex tasks require coordination among multiple Agents—akin to how a company or team operates. For example, optimizing the entire transportation system of Manhattan necessitates a Multi-Agent System (MAS): • Imagine that every driver, pedestrian, and traffic signal on Manhattan Island is an AI Agent: • Each smart driver has its own goal: to reach its destination quickly and efficiently, while minimizing fuel consumption. • Each pedestrian has a different goal: to cross streets safely and quickly. • However, the goals of individual Agents may conflict with the overall system’s optimal solution: • A driver’s fastest route may cause congestion in high-traffic areas. • A high number of pedestrians crossing frequently could disrupt vehicular flow. At this point, interactions among Agents require Multi-Agent Systems to balance local optimization (individual goals) and global optimization (system efficiency): • Smart traffic signals dynamically adjust light durations to improve overall traffic flow. • AI Agents share data, helping vehicles proactively reroute when accidents or congestion occur. • Agents learn from the collective system to continuously optimize city-wide traffic efficiency. Key Features of Multi-Agent Systems—— Multi-Agent Systems are not just a collection of independent AI Agents but rather an integrated ecosystem with coordination mechanisms: • Balancing Local vs. Global Optimization – AI Agents adjust their individual strategies based on overall system needs. • Task Sharing & Collaboration – Agents interact dynamically to improve task efficiency. • Self-Learning & Evolution – Agents continuously train and upgrade themselves, adapting to complex environments over time.

The systems are broken and it's up to us to build new ones.

@agravityAG 这是我今年投的第一个项目，可以关注一下