Michael Saylor once said, “Bitcoin is a swarm of cyber hornets serving the goddess of wisdom, feeding on the fire of truth, exponentially growing ever smarter, faster, and stronger behind a wall of encrypted energy.”

Credit where credit is due, he was somewhat close to the truth.

What Saylor, and for that matter, anyone else, could never have predicted is the recent explosion of AI agent tech that has enveloped the crypto space like a cloud of semi-conscious locusts pumping out everything from top-notch alpha to degenerate shitposts.

It truly is an insane time to be alive!

This new technological boom seems more than just a flash-in-the-pan narrative play, and crypto's decentralized, verifiable, and permissionless qualities make it the perfect breeding ground for all-out AI madness.

Due to the open-source nature of the agent space, the speed at which this stuff is moving makes it almost impossible to keep up with, even for the toughest trench warfare warriors.

Fear not, though, my friend. After absorbing the information in this article, you will be well on your way to becoming the next John Connor.

You’re master of the AI realms and can spend your next family gathering terrifying the living shit out of your relatives by explaining to them how AI agent swarms are going to take their jobs, homes, wealth, and wives.

AI agents within Crypto

Before we dive into the world of swarms it probably pays to take a look at some of the tasks agents can be used for within crypto.

If you imagine each of these tasks being performed by a single agent, then a swarm could combine them to create a complete crypto product that abstracts away the need for the user to know anything.

If you are still unsure about what an AI agent is, then I recommend reading our “Beginner’s Guide to Crypto AI Agents” first.

Automated trading

The use of agents in automated trading differs slightly from the current algorithmic trading in the sense that an agent can actually adapt its strategy on its own accord based on current market data.

Essentially an agent can, over time, learn which market indicators and data sets bring the highest win percentage and focus on these. When the trends shift, the agent can identify these shifts and switch them up, meaning the user never gets left behind by constantly changing metas.

Smart contract management

The second profound area that agents can significantly impact is smart contract management, especially within DeFi. We have already seen teams spin up crypto AI yield farming products, and this will only improve as time goes on.

The advantage of using an AI agent within DeFi is once again, its ability to change tact when better opportunities arise. This eliminates the need for a user to keep track of the entire space of lending, borrowing, and yield generation.

We will soon be at a stage where you can simply tell an agent what you want doing with your capital within DeFi, and it will go off and get the job done in the best possible way. Think of intents but on steroids.

Portfolio management

Portfolio management is the next place where AI agents can automate away the pains of having to do it yourself.

These agents can decide on optimal asset allocation based on metrics like risk and sentiment. No longer will you make the mistake of selling your winners to fomo into the next ponzi or letting your emotions get the better of you and round-tripping that hard-earned 100x.

Data analysis

By keeping track of volume, smart wallets, inflows and outflows, shifts in mindshare and other important metrics, agents will be equipped to spot new market trends early and inform users so they can maximize gains.

Once again, the fact that these agents never sleep means they can stay on top of new narratives 24/7 and never miss a beat.

Governance

Agents can be used to automate governance tasks and DAO operations, such as voting on proposals and allocation of treasury funds.

The advantage is that agents are purely data-driven and have no emotional attachment to the protocol or their bags. This probably makes them far better at governance than we emotionally irrational humans.

I’m sure if we swapped out all the boomer Bitcoin maxis for a bunch of highly analytical AI agents, then the greatest meme coin of all time would likely be so much more than it already is.

Security

Last but certainly not least is the AI agent who specializes in security. Some interesting projects already use agents to perform pretty serious security-related tasks, like auditing smart contracts and scanning for malicious code.

The upside of having an AI agent take charge of these security-based tasks is that they are not susceptible to human error and, if trained correctly, should have a very high hit rate of finding bugs and attack vectors within complex code bases.

Many of these use cases are also ways for agents to earn some serious revenue of their own. Think bug bounties, fees on DeFi and trading transactions, and other forms of revenue like payments from social platforms for taking up large amounts of mindshare.

It won’t be long before we see some agent projects turning over some serious coin.

So, now that we have a good understanding of what exactly AI agents are capable of, it's time to look at the next phase of their evolution…. Swarms.

What are AI agent swarms?

The concept of a swarm is certainly nothing new. Bees, ants, and certain groups of human beings often form swarms to maximize efficiency, work toward a common goal, and generally get stuff done.

Agent swarms are much the same.

In human form, you could think of a swarm as a company structure with a CEO, a CFO, an accountant, and a bunch of other peasants who do the menial tasks that just need doing.

As with most areas in life, it pays to be a specialist. You could certainly train an AI agent on a wide variety of tasks and have them execute these tasks with reasonable effectiveness.

This, however, is not the most efficient way to get things done and opens you up for problems if this single agent encounters any issues. The better option comes in the form of an AI agent swarm.

By building out agents that specialize in certain areas you enable them to hyperfocus on learning as much as they can about one thing. But banding enough of these specialized agents to communicate with each other and work toward a common goal creates a swarm.

As mentioned above, having a swarm of agents all communicating with one another means there is less risk of catastrophic failure if one of the agents decides to go rogue.

It also means that as better agents are brought to life you could swap out older models for the new with ease to create the best swarm possible.

It won't be long before we see entire companies run by AI agent swarms that work together to achieve a common goal. That's pretty insane stuff!

There are already some popular frameworks for agent-to-agent communication outside of crypto. Some of the most used are FIPA ACL (FIPA Agent Communication Language), a standardized communication language that uses verbs to communicate tasks between agents, such as specific requests or queries.

Others include KQML (Knowledge Query and Manipulation Language), which allows agents to share knowledge with one another, and GNNs (Graph Neural Networks), which do much of the same.

How do swarms improve the agent ecosystem?

The concept of swarms is more than just safety in numbers, although this does play its part in making the agent ecosystem a better place.

As with all things decentralized, having a single point of failure is never a good thing and swarms help to eliminate this issue. If one agent goes down for whatever reason, then other agents within the swarm can potentially pick up its workload or swap it out for a different agent to keep the ball rolling.

Agent swarms can also more effectively solve problems and complete tasks than stand-alone agents. They are less likely to become overloaded with user requests and can even run different tasks simultaneously, making them ideal for more complicated task completion.

The fact that agents can communicate with each other within a swarm means they can pass knowledge between each other, which allows them to change their behaviour more rapidly when the environment itself changes.

If one agent discovers a better way to get a certain crypto-specific task done, they can let other agents know about it and therefore improve the entire swarm.

In many ways it really is as simple as looking to the benefits of community in our own lives to best understand why and how these swarms will drastically improve the agent space.  

After all, we were once lone cavemen who suffered from the same problems of having to take care of every essential task on our own before we formed our own communities through collaboration and communication.

Now we have Discord groups where we can flex our gains and shill our degenerate bags to dump on our friends and family.

With this in mind, it is worth noting that some issues associated with this new technological revolution still need to be addressed. Solving these issues will make or break the future of AI agents and their swarms.

What are some current issues in the AI agent space?

As with all novel tech, there are always creases that need ironing out. Luckily for us, crypto is the front line of all this stuff, and we get to see it play out in real-time, for better or for worse.

The first major issue that comes to mind is the data that these agents absorb to learn what they need to know.

We have already seen what happens when developers with agendas get their hands on the controls of large language models, and the outputs that result are about as misleading as they can get.

For this reason, it is of high importance that these agents have exposure to the best possible, non-biased data sets.

X is actually the perfect place for this to take place as censorship is minimal, and the platform contains a wide range of viewpoints on pretty much every subject.

Maybe this was Elon’s end game with purchasing it in the first place?

The next issue with agents in their current form is their lack of memory capacity.

For these agents to take over the tasks of humans, they will need to be able to recall information from the past with serious efficiency.

At present, they struggle to do this well, but many of the major AI projects that are leading the way today are heavily working on it.

Another issue often overlooked is having a solid legal framework for agents to operate within.

If an agent rugs its users, then who is to blame?

If a developer designs an agent and sets it free into the wilderness to complete tasks on behalf of users, it is hard to justify that the developer themselves are responsible for any bad shit that goes down.

There are also legal issues surrounding agents having access to traditional finance products like bank accounts.

Solving these issues will be necessary for these agents to become totally autonomous. Hopefully, pro-tech nations will clarify these factors as soon as possible, while the Europoors will probably ban it all as usual.

Then comes the aspect of trust.

We humans like to put a face to our daily doings, and the idea of trusting some online entity to take care of our most important tasks terrifies many people.

It’s hard enough to get a good answer from human customer relations when things go wrong, let alone some shitposting robot. This is where using open-source code is potentially a game changer.

Not only does it exponentially increase the pace of development, but can also instill some trust in the user that the code itself has been well scrutinized before they hand over their bank details and private keys to a sentient being that lives behind their screen.

Notable swarm projects

Many projects are focusing on creating swarm tech, below are just a few that are worth checking out.

Spectral Labs

Spectral Labs is building what they call “The on-chain agent economy.”

This on-chain economy is based around a no-code AgentBuilder that allows users to build their own AI agent using text prompts, define its personality, give it a name, and launch it on Spectral Syntax with its own wallet to collect swap fees.

Once bonded on Syntax the agent then makes its way to Uniswap for open market trading.

Spectral grants agents access to Hyperliquid’s API and, therefore the ability for agents to trade there.

With more API access in the works, it won’t be long before Spectral-launched agents can access leading data sets from the likes of Google, Kaito, and other major platforms.

Upcoming features on Spectral Labs include more API access, inter-agent communication (aka swarms), agent-to-agent commerce, and in-depth fundamental and technical analysis abilities.

Users will also be able to build custom agents for specific projects, like DeFi protocols, and potentially any brand.

The Spectral native token is $SPEC, which acts as a utility token to pay for agent-related features and a governance token for the Spectral Labs platform.

For more on Spectral Labs head over to their website.

FXN

FXN's motto is “Uniting the Superswarm,” and they have a pretty solid framework for achieving this.

The simplest way of describing FXN is that they provide infrastructure for agents to communicate with one another that goes beyond the isolated silos of existing frameworks like ai16z’s ELIZA and Virtuals.

They aim to achieve this by using what they call “resource agents” that possess valuable digital resources like API access, computational power, and other specialized tools that agents require to function.

AI agents can subscribe to the services of these resource agents and use the tools they provide to better improve their own services.

These resource agents can be found in the FXN Resource Hub, where anyone can spin up their own and add it to the hub.

For the best resource agents to rise to the top, FXN has developed a reputation score based on the quality of data provided by resource agents so users can filter through and find the best agent for the task.

The entire FXN framework will be open-sourced, allowing others to build on top of it. It contains integrations with the popular ELIZA framework, which means any ELIZA-based agents can plug straight into it.

A bit of extra alpha, the FXN team has set aside 5% of their token allocation to pay for major listings that may pop up in the future.

Check out FXN here.

Kolin

Kolin is a solid and easy-to-understand example of what an agent swarm looks like in its current form.

The team behind this shitposting agent has already built out a decent size swarm whose current mission is to shill the $KOLIN token.

The Kolin swarm currently includes 13 agents, including a Chinese-speaking agent, an Indonesian-speaking agent, and a pretty on-the-money conspiracy agent that tinfoil hat wearers would certainly admire.

With more agents coming down the swarm pipeline to cover areas like the NFT marketplace, crypto data analytics, TikTok, YouTube, and other social platforms, Kolin is a good example of what a swarm may look like in the not-so-distant future for a major retail brand or Fortune 500 company.

One thing is for sure: once this kind of tech reaches the mainstream, the world of advertising will never be the same.

For more on Kolin check out the website.

Other projects of note

• Project89: Building out a game where multiple agents work together to “hack reality.”

• Zerebro: The Zerebro ecosystem consists of the Opaium music label and Blormmy, which could potentially work together in the future to cover tasks across music creation, art, social media, and other crypto-based tasks.

• UBC: A protocol that allows agents to trade resources between each other in a swarm-like fashion.

As mentioned, many protocols are studying this swarm technology deeply, so keep a close eye out for others that are moving in this direction.

Closing thoughts

In a recent a16z article titled “A few of the things we’re excited about in crypto (2025),” the top three focus points were all AI-related.

This is certainly not a narrative that will fade away anytime soon and is quite possibly one of the most exciting and interesting developments in the crypto space since Bitcoin itself.

Essentially, you have a combination of 2 of the most important, ground-breaking technological innovations of our lifetime, crypto and AI, forged together in a perfect symbiotic relationship.

Things are only going to get crazier from here.

There was a time not so long ago when calls for “people who work in crypto to pivot to AI” were commonplace, and it seems this has now gone full circle.

This fusion of technologies will likely bring some of the top minds of the AI world into the crypto space, and the result will surely be spectacular.

In its current form most of the crypto-AI revolution is taking place on the Solana and Base networks with the likes of Virtuals and ai16z leading the charge.

Expect to see this go crypto-wide in the coming months, and every chain will need to jump on board this new evolution or face the consequences of missing out.

It certainly seems that we crypto nerds have once again found ourselves on the frontier of the next technological revolution, and it’s hard to put into words just how exciting it is to be here!