AI Agents Are Promising, but AI Agents Working Together Could Be Life Savers

For all the talk of AI gaining some sort of collective intelligence and turning against us, those in the industry will tell you just how incredibly hard it can be to get a single AI model to work, let alone have several models work together. It's a fascinating thing to consider, like zombies or vampires, but in reality, the world just doesn't work that way.

As a matter of fact, we'd all be a lot better off if AI models working together were a reality of our daily lives.

Unlike a screenwriter glossing over the countless details of AI models working together to form a collective intelligence, the details matter. What they do focus on is the "soul" of this giant AI, and its emotional intent to achieve some grand vision. Both parts of this couldn't be further from the truth, but they do make for great entertainment.

That said, one element of this that isn't complete fantasy is the ability for AI models to work together. The realistic part is that it is incredibly difficult, that we are still learning how to do it, and that it doesn't create some sort of emotional motivation for the combined AI. But as we look at this possibility, we realize very quickly that combining AI models is, in fact, very important for the future of humanity. Why? Because, among other things, it might unlock the key to curing cancer. Let's explore.

The Challenge of Scalable, Multi-Agent Systems

To really understand the challenge and complexities of a multi-agent system, let's look at a single AI model. In some ways, building an AI model is like designing and constructing a building. First, you need to understand the purpose, or what problem it's trying to solve (is it a house, a school, a hospital?). Then you need to select the right building material, the right architecture, the right location, and understand the cost of both building and operating it. With AI models, you need to understand the problem, then use the right architecture and building blocks (what type of model, how many layers and nodes, what type of activation function, etc.). You need to understand the data that will be used to both train and operate the model, and what it will cost to obtain it and maintain it. All of this is possible with today's knowledge of AI and computing technology. But it isn't easy.

Now, think about what it might take for various AI models/agents to interact. Each one is a unique creation, built for different purposes, with different syntax and languages, using different data. Unless you have a way for these agents to understand who they themselves are, then understand what the other agents are, there is no hope in collaboration. Think back to the building analogy. There is a major difference between an organized city that was designed as a holistic community and a group of buildings that have no idea what the others do. In order to build something bigger and more connected, we need some help.

Coordinating Agents

Thankfully, this isn't a new problem, and it's easy to see the value of solving it. There are many groups working on this, although the furthest progress might have been made by Coral Protocol. The Web3 platform is using some of the key strengths of blockchain, including decentralized and autonomous interaction, agent-to-agent payments, and the ability to scale up significantly. Additionally, Coral Protocol's token has recently launched on MEXC exchange with great success. Their whitepaper on the protocol goes into more detail, but some of the key elements focus on the list of obstacles any multi-agent system will face.

There are a number of current technologies developed in this field that Coral is using to create autonomous interaction:

• Model Context Protocol (MCP) ensures that agents can find new tools to use, understand their purpose, and employ them without having to completely rebuild themselves.
• Agent Communication Protocol (ACP) is an effective "language" that helps set the rules for how agents interact; they know how data is structured, how to communicate their role to each other, and they can use multi-modal data, which expands the types of agents that can participate.
• Agent-to-Agent Protocol (A2A) gets down to the details between agents, helping them see what the other's goals are, capabilities are, and can show how they could work together to solve a bigger problem.
• Agent Network Protocol (ANP) is one of the keys to scalability, as it allows agents to search a network for other agents, find them, and then use the other protocols to interact and "recruit" the new agent if they fit what the larger problem requires.

So what can these different multi-agent elements do? It depends on the platform using them, but in Coral Protocol's case, it builds an ecosystem where you have agent creators on one side, building agents that are capable of many different tasks; on the other side, you have those wanting to solve large problems, and they use these different agents to collaborate, build up the right team of agents, and solve the problem. The agents can understand who needs to accomplish what task, who is best suited for it, and how the problem-solving should be structured.

AI Agents Saving Lives, One Medical Trial at a Time

As mentioned above, the result of all this is a new way to solve gigantic, messy, and complex problems. One such problem that has challenged humanity has been the time, cost, and specialized effort needed to complete medical trials. The workflow of such an endeavor is incredibly dense, has many potential problems along the way, and the result is huge costs over a long period of time. Could AI agents working together make this process better? Absolutely.

If the large problem is to "complete a medical trial," we need to break that down into smaller and smaller pieces until we get to a level of detail appropriate for an AI agent to handle. What is a medical trial? It is a combination of data collection/analysis, parsing different studies, anomaly detection, developing summaries of statistics and overall results, risk analysis, recommendations, and tracing all data throughout the process. Many of these steps are ideal problems for AI agents to solve, as they love to analyze, find anomalies, give recommendations, generate summaries of large information sources, and more. By using a platform like Coral Protocol, these different agents could develop a workflow that combines the many different talents of AI agents, and could do so without the messy and slow human intervention between each step. Instead, the human team is responsible for creating the overall architecture of the problem and its components, and then guiding the collection of AI agents to self-organize, delegate, and solve each piece of the puzzle. When you break it down to this level, what seemed like science fiction now seems completely feasible.

Looking Ahead

The next few years will be very exciting to watch. AI has certainly not slowed down over the last decade, but multi-agent problem solving is truly the next step in evolution. We will quickly see the problems that AI can solve jump in size and complexity, and previously unsolvable social challenges will suddenly have answers. It won't be easy or without obstacles, but for the first time, we are on our way to large, AI agent communities. However, instead of being the scary monsters of science fiction, these AI agents are working with us to solve humanity's problems.