It’s no secret that Solana enjoys its meme coins. For quite a while now, it has gained a reputation as the go-to chain for meme coin trading, in no small part because it has the features that every meme coin trader needs: high speed, low cost. Fast and cheap is the name of the game, and this has worked well for the Solana ecosystem. In fact, when Trump and Melania decided to launch their own meme coins in January, not only did those tokens explode, but so did memes in general. As a result, Solana saw a nice spike in both volume and value as well, and one that didn’t fade like the meme excitement once the president’s inauguration was over.
It’s great to have a niche market, and Solana certainly does with meme coins. That said, let’s not be hasty in giving labels to the chain, especially since they aren’t trying to be a chain just for meme coins; they are working to be the fastest and cheapest chain, which creates opportunities on a much larger scale than meme coins.
Yes, it’s fun to speculate and trade on the latest trend. It’s fun to buy some tokens simply because you appreciate a good joke, even if it’s childish. But this level of speed and low cost is creating use cases that are much more “grown up” in not just the crypto world, but in the global ecosystem. Let’s look at one in particular from Coral Protocol, and how Solana’s extreme efficiency in connecting an entirely new society of AI agents.
The Internet of Agents?
So what is an “Internet of AI agents,” exactly? To understand this, we need to have a very high level look at what an AI agent is and what an AI model is.
For any AI, you need to understand what problem you are trying to solve. Do you want to show a photo and classify it as a dog vs. cat? Do you want to examine housing data and predict sales prices for next month? Do you want to go from one point to another in your car, and get there the fastest route possible?
AI models are built for each one of these tasks, but to do the job, they need the right data to train on and understand how to solve the problem. The model needs to be the right type and structure (entire books are written on this step), and the model needs to be tested to make sure it performs correctly. An AI agent is somewhat different from a traditional model.
Like a model, the agent uses a data set to train, but unlike a model, the agent is built using different tools specifically designed to focus on this type of AI. The tools will help to narrow the focus of what that agent should do, help to tie in the training data, and most importantly, help to tie in the continued data used by the agent to both learn and interact.
At a very high level, an AI agent is built with a specific job in mind, connected to training data, and connected to API links and other systems. The training data set will feed the AI agent the right mix of data needed for it to learn the basics of what it should do (customer service, FAQs about a given website, searching financial feeds for specific patterns, etc.). The agent learns how the data elements are related to each other and how to make sense of these connections. Then the agent is launched and begins doing its job: learning with new information it receives; interacting with either humans, systems, or other agents; and evolving through this experience.
It’s not unlike how humans learn in school: we take in a lot of information, and as we do, our teacher helps to explain its context, the relationships between concepts, and how we can solve problems and answer questions using this knowledge.
As we continue learning after school, taking in new information, our initial training helps us to know how to use that information to solve problems, and we evolve over time. Congratulations, you’re basically a very slow AI agent! Okay, humans are more than that, but within a certain topic, there are a lot of similarities in how we learn, how we process the information, and how we use it to answer questions.
An AI agent is usually created with a certain job in mind so that the cost of training it and gathering the data needed can be minimized to that specific topic. Again, this is similar to humans: Yes, we know about a lot of things, but if you consider your job, you typically learn a lot about what you need to do, but you don’t spend years training how to do your colleague’s job. We are specialized in our knowledge and what job we do, and so are AI agents.
Back to AI agents. Now that you have an idea of what they are, it’s easy to see why there are thousands of agents scattered around the internet, each in its own silo to answer questions about a narrow topic. True, an average agent can’t answer a wide range of questions like ChatGPT, but it can be an expert in a specific area, and more importantly, it can be built cheaply and quickly. The problem is, like a good spy story, these agents work alone and don’t play well with others.
This is where Coral comes in, and where Solana’s ability to allow fast and cheap transactions makes it possible. Coral can do a number of really interesting things with AI agents, no matter who built them, how they built them, or why. The platform uses a variety of tools and a standardized interface called Model Context Protocol (MCP). MCP is a true game changer for AI agents, as it allows a common language for them to interact with each other and with sources of data.
Originally, an AI agent built for a specific task might normally only be able to connect with a database or API it was designed for using bespoke code. With MCP, the AI agent can use this standardized language to connect with countless APIs, databases, and other connections by using this agreed-upon format. This allows an agent to connect with new data sources over time without having to coordinate the connection or rewrite code; instead, it can reach out to the data source, ask for what it needs, and the data source (if it is also using MCP) can understand and provide it.
This paves the way for what really creates the “Internet of Agents,” which is the ability to coordinate multiple agents into workflows, manage memory and scope, and create context-aware communication. Coral has created agent discovery and registration so that these AI agent teams can find the missing member with the skills they need. In order to create a layer of trust, the protocol provides rewards and incentives to reward trustworthy behavior among the agents to weed out poor performers and bad actors.
Vast Implications on Solana
All of this infrastructure was built on Solana to take advantage not of its meme coins, but of its fast speed and cheap transactions. Agents searching for each other, self-organizing, and problem-solving take a lot of processing power, which would be incredibly expensive on other chains. However, a fast and cheap chain makes this futuristic use case possible. Not so childish now, is it?
So, what can be done with the Internet of Agents? Considering just how many agents are already active, the diversity of what their specialties are, and how many new (and better) agents are launching daily, it’s hard to pinpoint what can’t be done here.
Seemingly, with any problem, you would simply need to understand the different components well enough to form a team of the right experts, incentivize them to work together, coordinate the subtasks and larger coordination of work, and produce the answers you need. And keep in mind, this isn’t a theoretical use case years down the road; this is happening now, and is only going to grow and improve.
What’s Next?
What comes next is basically inevitable at this point. The ecosystem is working, dev teams are getting rewarded for creating quality agents (especially when those agents can be sold to many buyers for different applications), and the infrastructure has been built to get them coordinated and working together.
At this point we get to see larger and more complex problems finding solutions through self-organizing AI. As long as we are nice to the agents and always say please and thank you, we should be just fine.
Also Read: Sui vs Solana: Similarities, Market Sentiment, 2025 Price Targets
