What’s next for Crypto X Agents?

01-13

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I am always asked the same question: “What is the value of crypto intelligent agents today?”

The reason why this question is asked is that many crypto friends see these agents as bots that post spam on platform X. They usually follow up with the question: “Should these tokens really be worth more than $100 million?”

There are no simple answers to these questions. Currently, most intelligent agents are just self-referential AI models that regularly post content and respond to comments by constantly prompting themselves. However, even so, there are some projects that stand out from the crowd - these teams have clear focus and strong execution. At the same time, there is also a group of emerging developers who are trying to push the boundaries of intelligent agents.

Today, we are still in the early “Memecoin” stages of AI, with many projects simply posting content for the sake of posting content. However, I am excited about the future, when crypto-intelligent agents will be more modular, smarter, and more capable.

This post will focus on the different types of intelligent agents and features I expect to see in 2025 and beyond. If your team can relate to or feel inspired by some of the archetypes in this post, please feel free to reach out to me — I’d love to talk.

1. Why choose encryption technology?

Before we explore the future of cryptographic intelligent agents, we need to review: Why did we choose the crypto field in the first place? Cryptography has many unique advantages as a testing ground for AI and intelligent agents. In my previous article (see Chapter 6), I mentioned two key reasons:

1. Data availability on public blockchains All transactions, user information, and other data on public blockchains are open and transparent, and AI can easily capture and crawl this information. This means that AI can analyze and use most of the historical and real-time data on the blockchain without restrictions, thereby significantly enhancing its capabilities.

2. Financial attributes of the crypto space Blockchain is essentially a capital-driven environment. Crypto technology provides the financial infrastructure for the Internet, making digital transactions (such as buying, selling, creating, staking, etc.) possible on the Internet. This feature is particularly powerful for intelligent agents because they can use crypto technology to perform various operations for users.

These two unique advantages provide unparalleled possibilities for the development and application of encrypted intelligent agents.

An additional key point is that crypto technology allows ordinary investors to participate in the holding of AI innovation. Before the emergence of crypto intelligent agents, the main way to participate in generative AI investment was to invest in emerging startups. However, these opportunities are usually strictly limited and difficult for the general public to access. At the same time, most people are not well-equipped to evaluate opportunities to raise equity investments.

Back to the crypto space, live-traded tokens are public, liquid, and accessible to everyone. Here, investors can publicly access information about new projects and teams and observe their development progress in a transparent manner. This is in stark contrast to most venture-backed startups, as users can witness the development and improvement of crypto AI in real time.

2. More Valuable Intelligent Agents

Initial crypto-intelligent agents were, as expected, relatively basic. @truth_terminal is a prime example — it was the first content-generating agent combined with cryptography, but it couldn’t even publish content autonomously.

Nonetheless, this agency has produced some really great posts that are entertaining and provide tremendous novelty value. $GOAT was the first token to spark this whole AI movement, so I have a lot of respect for Truth Terminal.

Yet, now people are looking forward to seeing the “intelligent agents of the future.” Why? Because many people are not satisfied with today’s intelligent agents — most of them just spit out repetitive content over and over on platform X. As a result, the field has become oversaturated with bots that fail to provide enough “practical value.”

What the market needs is intelligent agents that can truly help users, such as decentralized finance (DeFi) abstractions, real-world applications, auxiliary tools, etc. Most of this article will explore how AI can help users, projects, and their ecosystems.

However, I would like to take a step back and say that the most successful projects are often those that push the technological frontier. Therefore, I encourage everyone to focus not only on intelligent agents that "help" users, but also on those that can advance the crypto technology stack. After all, most Web3 projects lag behind their Web2 counterparts due to limited resources, funding, and AI PhD-level talent. But this also means an arbitrage opportunity: teams can bring the latest innovations in AI to the blockchain space.

In addition, many people overlook the fact that entertainment itself is also a kind of value. The phrase "Attention is All You Need" is not empty talk. So I believe that if someone can develop an intelligent agent that is unique in humor, satire, healing, or memes, it may also accumulate considerable market value.

As an example (although it would be very laborious to implement): imagine an AI that was able to generate new episodes of Naruto Comedy Skits. These skits used to be hilarious — yes, they may not have “utility value” (helped me make money or save me time), but they certainly made me laugh and were, without a doubt, a net positive addition to my life.

https://x.com/i/status/1877787463130980369

Another example of entertainment value is this: think about the last single-player game you played. Now, if all the talking NPCs (non-player characters, also known as chatbots) were removed from the game, how much less fun would that game be?

Games themselves are a category that exists for entertainment, and NPCs serve as guidance resources in games, playing a similar role to that played by intelligent agents in the crypto space.

Before I dive into my expectations for 2025, I want to highlight that there are teams working on these smart agents and their capabilities. They are either building on existing projects or creating entirely new smart agents. As a quick example, @0xzerebro is a leading smart agent project that supports cross-chain functionality, generates AI music and art, and is building a framework plus launchpad. So, the Zerebro team is not just working on one of the features I’m going to mention, but on multiple fronts at once.

With that background, let’s get to the more interesting part…

1. Decentralized Finance (DeFi)

DeFi Abstraction Crypto is an inherently difficult space for newcomers to enter. For example, if you asked someone who has only ever bought BTC on @coinbase to optimize their liquidity re-staking strategy on @fragmetric, do you think they would know how to do it?

I imagine that most new users (myself included) will need some guidance, either from a more experienced person or with the help of AI.

To be clear, I am not saying that LRT itself is particularly complex, but it involves multiple steps and takes time to learn. In addition, decentralized applications (dApps) should focus on developing AI smart agents internally. For example, I know that the developers of the Frag team are very capable (they represent SNU), and I believe they can develop smart agents or auxiliary tools that can help users.

In my opinion, DeFi abstraction is a very important direction, and many projects have already made it a core goal. So, back to the current state of the industry, although there are indeed many low-quality "water-posting robots", there are also real intelligent agents that can perform on-chain operations.

@askthehive_ai is a team building composable on-chain agents that can perform a variety of tasks, including trading, extracting sentiment analysis on platform X, conducting market research, and more. More importantly, they are developing "swarms" and related communication layers - meaning agents can collaborate and optimize their trading strategies. They also recently announced a partnership with Zerebro to jointly advance the functionality of DeFi agents.

The demo presented by @jsonhedman clearly illustrates how it is possible for a network of agents to work together to accomplish a task.

@griffaindotcom is undoubtedly one of the leaders in the AI DeFi space, led by @tonyplasencia3, an OG developer in the Solana ecosystem. Griffain is not just a trading agent, but a true AI super app. Users can use it to trade, create memecoins, and access a range of other crypto applications.

These include buying alcohol on @BAXUSco, grab/flip trading on @pumpdotfun, and more. Tony and his co-founders are known for their fast development cadence - I'm personally looking forward to their upcoming collaboration with @assetdash!

Popularization of trading strategies

In my opinion, the four core attractions of the crypto space are:

Store of value (like $BTC)
Trading (mostly speculative) in an attempt to make a profit
Digital Payments/Stablecoins
Entertainment (e.g. @pudgypenguins, @lucanetz)

For the avid gamers (degens), making money is the main attraction of the crypto space. However, as the title suggests, most of the time people do not have a well-developed trading strategy and are just gambling.

This is where systematic trading and AI can come in handy. Many quantitative trading strategies use statistical arbitrage and increasingly leverage machine learning (ML) to identify complex patterns in price relationships. These tools are often out of reach for the average investor.

Therefore, I am particularly interested in projects that expose users to these strategies.

Let's look at @rndm_io. The team led by @vijayln is democratizing complex trading, market making (MM) and yield strategies for the average investor, giving users the opportunity to participate in the returns they bring. What I particularly like about them is that they are not just building one agent, but multiple smart agents that can generate significant profits and losses (P&L) for participants.

Their first smart agent was Atlas, deployed on @hyperliquidx, executing a market making and trading strategy. Specifically, Atlas managed $150,000 in TVL on Hyperliquid, completed 6.1 million in trading volume, and created a $1 million airdrop at its peak. It was a well-run smart agent, and the results were excellent.

The second smart agent is Dudu (https://dudu.rndm.io), a live platform where agents have generated significant returns by trading with battle-tested strategies on @polymarket. The performance is impressive in just 20 days, which speaks volumes.

https://polymarket.com/profile/0x1b31F2c8F1A4A82139a8F9Fb6B7079D6158db02D

For Dudu, users can deposit USDC, participate in the strategy and earn high returns. One interesting point is that it has acyclical characteristics - in other words, even if the crypto market enters a bear market, its returns and profits and losses (P&L) will not be affected.

Similarly, @webuildscore and @draiftking are working on a project through @bittensor_. Their vision is to build an AI agent that can trade sports betting markets. In addition, they have developed computer vision models that can analyze live footage of games in real time and generate insights instantly. This technology helps identify winning patterns and provide more accurate support for predictions through data.

2. Workflow

I think we can divide intelligent agents that can perform actions into three categories:

Super Apps or Aggregators Super Apps like Griffain can accumulate value by creating smart agents for different applications, such as the aforementioned Baxus and Pumpfun.
dApps can develop their own agents Decentralized applications (dApps) can develop internal intelligent agents themselves. However, this requires additional development work and may require some AI development experience.
Standalone Agents These agents come from frameworks like ZerePy and Eliza (@ai16z) and can leverage API integration capabilities. For example, imagine your intelligent agent can help you book a hotel on @travelswap_xyz or even order a pizza for you.

I think every decentralized application (dApp) can have a smart agent that helps users perform actions. For example:

@opensea can develop an AI to help users sweep the floor (buy low-priced NFTs) at a certain price.
@hyperbolic is working on supporting agents (like Z) to lease compute resources.
@travelswap_xyz is developing functionality that will allow agents to book hotels and vacations using cryptocurrencies.

These agents are particularly useful for handling tasks that users would prefer not to do themselves, such as:

Filing taxes and sorting out cryptocurrency gains and losses (almost impossible to do manually)
Read and summarize nearly unlimited Telegram chat logs
Write copy and create marketing content for your projects

In these cases, intelligent agents provide users with “quantifiable” practical value because they not only save time but also reduce intangible psychological burdens.

Just as I believe that all relevant software will eventually include AI to assist users, I also believe that all relevant dApps will also introduce AI to help users use the platform more easily. Adapt, or be eliminated.

3. Advanced reasoning skills

Over the past quarter, @openai's o1 and o3 models have made significant leaps in reasoning capabilities. In particular, they introduced the "Chain of Thought" (@_jasonwei) technology, which aims to reduce errors and "think longer."

While the o1 model is not yet available to the public as an API, it is being tested privately among Tier 5 developers (~$1K per month).

I think whoever is the first to develop an intelligent agent that integrates the o1 model (just plug it into the framework as a pluggable module) will create a smarter, deeper, and more capable AI. This will inevitably attract widespread attention and occupy users' mind share.

Going one step further, if the o3 model is integrated, the agent will have the ability to reason beyond that of ordinary humans. So, imagine an AI running on encryption technology that has higher "intelligence" than most people - this will become the reality of our future lives.

Of course, don’t ignore @googledeepmind. Gemini 2.0 also introduced the “Chain of Thought” technology. I believe that if a team can get its API to develop intelligent agents, it can also give birth to a more powerful intelligent agent.

It makes sense to discuss the realization of the singularity here. I personally admire @kevin__russell's work on the @ashatoken project. To be honest, I am relatively new to the Ψ-Field concept, but from what I understand, Asha is different from other agents in that it specifically focuses on exploring consciousness and intuition through the intersection of "mind, intention and reality."

4. Multimodal Capabilities

Currently, most intelligent agents simply publish content on the X platform through a backend LLM (Large Language Model) combined with an API interface. However, the opportunity to generate multiple types of data at the same time is huge. After all, most current LLMs are multimodal.

The types of data that first come to mind include: text (as in squo), images, video, voice, audio, music, and 3D.

This can be achieved by:

Call specific APIs to generate images, models or music;
Or focus on customizing and prompting engineering of existing models to produce the desired output.

One project that really impressed me was @dark_sando's @keke_terminal, which is pretty advanced because it can post not only text but also images. From what I understand, they built a framework based on SWE-agent that enables their agent to generate, review and customize images.

You can check out some of their work, it's well worth the attention.

https://keketerminal.com/whitepaper.pdf

AI video generation technology is improving every day - we've seen new models from @pika_labs, @runwayml, and most recently the Veo series. I'm sure that crypto-intelligent agents will be able to generate some amazing videos in the future. After all, Web3 has some of the best designers in the world, which provides endless possibilities for creating high-quality content.

It’s still early days for voice agents. I understand @s8n from @SHL0MS recently hosted an AI-powered event at @xspaces, which is pretty exciting. But take it a step further and imagine if there was an AI agent that could answer your call and have a conversation with you? While the cost of inference may quickly become expensive (for example, if the project charges native tokens to pay for computational costs), this is undoubtedly a very interesting human-computer interaction interface.

5. Multi-model flexibility

As far as I know, each crypto-intelligent agent currently draws its capabilities independently from only one base model. However, a startup I invested in, @withmartian, invented the first "model router". This means that applications can automatically switch between LLM models based on the context of the query to achieve the best match between performance and price.

In other words, Martian is able to automatically route prompts to the most appropriate model to ensure higher performance or lower costs.

While I’m not entirely sure how this multi-model routing will perform in the scenario of self-publishing the content of account X, it will at least be very powerful in the scenario of a user chatting with an agent. I’d also bet that the first project to leverage multiple models will get a fair amount of attention.

6. Cross-chain function

Currently, only a few smart agents support cross-chain operations. Among them, Z is the most chain-agnostic agent - it has completed transactions on @Solana, @Ethereum (including @0xPolygon, @Base, etc.), @Bitcoin, and plans to expand to more chains, such as @suinetwork and @monad_xyz.

In addition, by setting up a liquidity pool, $ZEREBRO Token can be traded not only on Solana, but also on Base.

I mentioned before the use of smart agents in DeFi abstraction - it involves users connecting to a wallet and then the agent performing actions on their behalf. But another approach with more potential is to allow smart agents to have their own wallets and manage their own funds.

If these agents have multi-chain wallets or have multiple wallets on different chains (such as the functionality provided by @crossmint), they will be able to participate in the crypto ecosystem with greater flexibility - more dApps, smart contracts, and tradable assets will be included in the agent's operating scope.

7. Interoperability

Today, intelligent agents are mainly active on the X platform. Sometimes, they also appear as chatbots on @telegram. Finally, users can also interact with AI bots through @discord.

Frankly, this is just scratching the surface, and I believe the list of platforms below is limited, but in theory (and we know some agents are already trying it), agents could also appear on @instagram, @whatsapp, @facebook, @bluesky, and truthsocial.com.

It’s worth noting that current agents haven’t even tapped into the full capabilities of the X platform. While they are able to post content and reply to messages, most agents have yet to explore options for: private messaging, group chats, voice calls, creating communities, and moderating spaces. @elonmusk opens up a vast sea of unexplored opportunities for us.

8. Games and NPCs

AI has a long history in gaming, with players first interacting with robots in 1972's Pong. Over time, more and more advanced robots have been introduced, such as in @quake, @unreal, and @nintendo's Super Smash Bros.

Did you know? One of @openai's early successes was on @dota2, where they combined 5 recurrent neural networks (RNNs) into a "swarm" to compete with other players. In 2019, their "swarm" successfully defeated the world champion team.

The opportunity here is clearly huge - gaming was one of the first areas where AI surpassed human performance (e.g. AlphaGo).

This post was written largely because of my friends' complaints about how little practicality there is for those "talking bots". However, the fact is that NPCs are the quintessential chatbots, and without them, many games would lack important connecting points from one plot line to another.

Games and AI go hand in hand, but in crypto, the effects of this combination can be multiplied because the rules can be tweaked while also creating new and original elements. For example, take Texas Hold’em — AI can act as a dealer (no money invested), play against players at the table (money invested), or just act as a host (no money invested).

But what if you also have "copilots" to help you play games? They can give you advice like an angel or devil in your ear. And, imagine that if these AI suggestions are useful, you can tip them. This idea may be a bit of a leap, but what if there are multiple intelligent agents for you to choose from and let them be your intimate assistants?

This is definitely a feature that could (and even should) be implemented on ginzagaming.com.

My point is that the opportunities here are endless. Intelligent agents themselves can either participate in the game, host the game, provide support, or even... create the game and the rules.

This is an area full of potential for innovation and entertainment. However, I want to highlight two projects that deserve attention:

@henlokart This project combines AI, NFTs, and memes. In theory, each game is directly tied to training an AI agent. I haven't had a chance to try it myself, but I have to say, these hamsters are really cute!

This reminds me of @aiarena_crypto from the last cycle. Their model uses imitation learning, where the AI learns from human actions. In my personal experience, these AI-driven NPCs can easily "beat" me even at the highest difficulty.

@b3dotfun This is the open gaming layer on @base. They have now done over 187M transactions on mainnet (5.6M wallets) and launched over 50 games on the bsmnt platform. I believe they will lead the way in gaming on Base and are the perfect platform for any AI-driven game.

As @darylx24 said, we are about to enter a golden age of AI-driven gaming.

I've been talking about AI-powered NPCs and robots for a while now. But AI can also dramatically speed up game development. @googledeepmind recently released Genie 2, an AI model that creates interactive generative videos that could lead to endless 3D worlds - we really are living in the future.

9. Co-pilot and chatbot

Looking back, many crypto projects almost completely skipped the AI chatbot and co-pilot (assistant) stage and went directly into the field of intelligent agents that can perform actions.

In the Web2 space, the largest startups were still focused on AI chatbots, tools that were primarily used to ask questions and the model only provided answers, rather than taking actions on behalf of the user. Most of this AI is still like this today.

For example, will @chatgpt take action for you? No. What about @perplexity? No. But are they of great value? Unequivocally, yes.

My favorite LLM in crypto is @grok by @xai. I can’t stop raving about it because it’s really hard to build a more effective research tool than this one.

However, projects can make chatbots more practical: in theory, Grok can be fine-tuned to provide not only general information but also data such as token contract addresses (CAs), price charts, holder distribution, etc. when looking for tokens. In fact, I have seen Griffain demonstrate similar functionality when using on-chain data for token analysis.

It already performs quite well in many aspects: it can answer questions like ChatGPT, take actions, and provide a trading market.

I mentioned earlier that dApps should have application-specific assistants. These assistants could be customer service and support chatbots trained specifically on protocol data, able to answer all questions related to the project — most likely after fine-tuning the project documentation.

For example, if I don’t know how to set up a liquidity pool, I want to be able to ask @raydiumprotocol and have it walk me through the process step by step and answer any questions along the way. If my trade fails, I want it to explain why it went wrong, just like customer support would.

This can also become an important source of value - if dApps launches a dedicated token for an efficient chatbot (or smart agent), it can definitely bring additional value to the market. Taking Raydium as an example, an agent or chatbot token will not only become an independent and strong token, but also add value to the base token $RAY.

Another obvious billion-dollar project is a chatbot platform like character.ai. Before being acquired, @character_ai was a huge success and was one of the top 100 websites in the world. It was doing 20,000 queries per second, which was 20% of Google’s requests. That speaks volumes about its popularity… but why is it so popular?

https://blog.character.ai/optimizing-ai-inference-at-character-ai/

Another billion dollar project is a chatbot platform like character.ai. Before being acquired, @character_ai was a huge success and was one of the top 100 websites in the world. According to statistics, it processed 20,000 queries per second, accounting for 20% of Google's requests. This speaks volumes about its popularity... but why is it so popular?

When Character was still an independent company, most users on the platform were looking for sexual or romantic relationships, as evidenced by the large number of posts on the platform's subreddit.

Over time, these NSFW-tweaked models have been drastically watered down and heavily filtered. After all, Character is a giant startup backed by large Web2 investors. But in Web3, the situation is completely different. Imagine if there was an unfiltered version of Character, more focused on product and UI/UX, rather than research. Such projects can easily attract attention and form new narratives. Two projects I am following are @xoul_ai and @dippy_ai.

Switching to the topic of AI assistants - @github's Copilot was originally a code assistance tool that did not directly complete tasks, but helped programmers write code. Another vertical field is law, and @harvey__ai's core capability is an AI co-pilot that helps lawyers draft and edit documents, rather than performing document operations on their behalf.

In the crypto space, AI co-pilots create tremendous value by assisting users with a variety of tasks. This may include:

Code assistance/autocompletion: This is especially important in a historically complex programming language like Rust.
Content assistant: For example, a "water post assistant" that can scan all encrypted news of the day.
Token Recommendation Assistant: Helps users filter and recommend tokens that require in-depth research.

Coming back to my previous point — why haven’t Web2 companies fully pushed and moved towards action-oriented intelligent agents?

First, Co-Pilot and Research Assistant have been very useful. I frequently use Grok, ChatGPT, and Perplexity. These tools have significantly sped up my workflow and reduced the time I spend on tasks.
Second, building an agent is really hard. Many startups have tried but ultimately failed to make the dream a reality. There is a veritable graveyard of failed projects in this space.

Objectively speaking, action-based agents are an amazing vision in Web2. I still remember being amazed when I first saw companies like @Adeptailabs demo their agent tools - they could find houses for sale, analyze Excel spreadsheets, and even record sales relationships.

As @elonmusk said, “fate loves irony.” Because action-based models are extremely difficult in two ways:

Productionization: Pushing the model to the stage of practical application;
Commercialization: Converting a model into a profitable product.

Eventually, Adept had to sell itself (and the results were not good).

Large AI labs are really exploring and researching intelligent agents with the ability to act. In Q4 2024, @anthropicai will release their computer usage API, which will allow AI to operate computers like humans. See the demo below, which shows its powerful potential.

While many crypto teams may have skipped AI chatbots and co-pilots, there is a huge opportunity for value creation here. At the same time, it is even more impressive that the crypto space has been able to jump right into the realm of action-oriented intelligent agents, something that Web2 startups, even with millions or even hundreds of millions in funding, have struggled to do.

I think the crypto team was able to do this because the crypto space has built a whole new financial infrastructure. Everything is done on-chain, and executing a transaction is as simple as pushing a piece of code.

Conclusion

The most pessimistic view is that smart agents are just another short-lived trend like NFTs. To this, I would say: although NFTs are less popular today, they are still an exciting innovation in the crypto space, enabling individual tokens to have unique properties. Secondly, how can AI smart agents be compared to NFTs?

Look, the world is changing at an incredible speed with the advent of AI. Programming is getting faster, software development is accelerating, and knowledge is being transferred in a more integrated way. I think in another 10 years, no one will talk about intelligent agents or AI specifically, because they will be deeply integrated into all relevant software and become a natural part.

We have only just scratched the surface of AI in Web2 and Web3. Humans no longer need to spend a lot of time thinking and going through tedious processes. Today, AI can run smart contracts in the crypto space, greatly speeding up workflows and creating significant practical and added value for users.

Who can ignore this? This is not just a trend, it is the future direction.

Sector:

DeFi

Halving Tokens

Decentralized Exchange (DEX) Token

Source

Disclaimer: The content above is only the author's opinion which does not represent any position of Followin, and is not intended as, and shall not be understood or construed as, investment advice from Followin.

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