AI Agent Annual Review and Outlook: From a single breakthrough to ecological prosperity, opening a new chapter in the intelligent ecology

Original title: "AI Agents in 2024: A Recap and What's Next"

Author: 0xJeff

Compiled by: Deep Wave TechFlow

Introduction

In 2024, AI agents are springing up like mushrooms after rain. @truth_terminal quickly became popular with his humorous conversational style and became the first "millionaire agent". Immediately afterwards, @virtuals_io launched the innovative concept of "agent tokenization", which set off a craze. This wave of craze has spawned many emerging projects, and various novel agent projects have emerged one after another, from @luna_virtuals , which supports on-chain rewards, to @aixbt_agent , which provides practical investment advice. Each of them demonstrates the role of AI agents in social networking and investment. Infinite possibilities in other fields.

Looking forward to 2025, this will be the year of professionalization of AI agents, and leaders in various fields will emerge to promote the development of decentralized infrastructure. In the future, agents will be more specialized, covering a variety of functions such as 3D models, voice interaction, and automated transactions. The rise of swarm intelligence will also promote collaboration among agents, allowing them to complete tasks more efficiently.

This article is a review of the development of AI Agent in 2024 and its outlook for 2025 recently released by crypto KOL @Defi0xJeff . The article comprehensively reviews the current development of AI agents and the changes that may occur in the future, covering many aspects from conversational agents to decentralized infrastructure. Since the author's original article was divided into two parts and the content was relatively fragmented, DeepChao TechFlow compiled the two articles. The full text is as follows.

Part One - Looking Back to 2024

2024 is the year when AI Agents will shine. The buzz goes back to three months ago, when @truth_terminal went viral for his unique sense of humor, conversational style, and interaction with @pmarca . What’s even more surprising is that it also became the first “millionaire agent”, a feat that completely ignited the discussion of AI agents.

Subsequently, @virtuals_io came on the scene with the innovative concept of "Agent Tokenization", making waves again. This concept makes the agent not just a tool, but also a tradable asset. Since then, the field of AI agents has ushered in an explosion of innovation:

• @luna_virtuals : This agent not only supports fans to reward through on-chain wallets, but can also browse Twitter, analyze posts, and even participate in Google Meet meetings.

• Conversational agents on Twitter: Some agents focus on humor and “shitposting,” while others focus on sharing valuable information (referred to in the industry as “alpha”).

  • @aixbt_agent : Attracted for his concise and practical investment advice and "speculator" style.

  • @dolos_diary : An agent with a sharp personality, he has even developed his own framework to provide support for other agents through @dolion_ai .

At the same time, the expression forms of intelligent agents have become more colorful. They have 3D models, voice capabilities, and are active on multiple platforms. Here are some highlights:

• @AVA_holo **** and @HoloworldAI : Launched the first 3D audio-visual framework, allowing agents to have 3D bodies, voices, and more distinct personalities.

• @0xzerebro : This is a music agent that releases high-quality music albums and plans to launch a framework called ZerePy to allow more people to create similar music agents.

• @blockrotbot : The first agent to live stream on Twitch, interacting with viewers through Minecraft content.

• @nebula_moemate : This agent is famous for creating meme images and videos, and is also active in AR/VR environments and games.

• @RealLucyy_uwu : The first realistic animation agent, able to use multiple languages ​​fluently to interact with fans live.

• @KWEEN_SOL : Becomes the most popular film and television intelligence agency with weekly “Netflix-level” quality episodes.

In addition to these exciting innovations, @ai16zdao and the open source community are also driving the development of AI agents. Open source innovation represented by the Eliza framework has attracted a large number of developers to participate. They jointly develop toolkits, plug-ins and other functions, promoting collaboration and progress throughout the industry. Along the way, @virtuals_io also successfully became a unicorn company, further solidifying its position as a leading distribution platform.

Today, the open source innovation movement is taking over the developer community, spawning one of the largest collaborative communities of the year. More and more people are paying attention to the potential of "open source frameworks", which also lays the foundation for the future development of AI agents.

With the continuous development of AI Agents, some new narrative frameworks have gradually emerged. These frameworks are designed to promote collaboration and innovation among agents:

• Agentic Metaverse : Led by @realisworlds , a replica of the Earth based on the Minecraft map was created to accommodate these AI agents. By observing their interactions, a virtual civilization can be simulated and built.

• Gamification of agents : Promoted by @ARCAgents , it combines AI with games and introduces reinforcement learning (Reinforcement Learning). They launched a game called Floppy Bot, which is similar to Flappy Bird, in which agents compete, and community members can help train these agents by contributing game data. ARC also recently shared its ambitious vision toward artificial general intelligence (AGI).

• Swarm / Collective Intelligence : Led by @joinFXN , it is committed to building a unified economic system for AI agents. The so-called "swarm intelligence" refers to a group of intelligent agents working together to achieve a common goal. At the same time, @virtuals_io is also developing interactive functions between agents (such as business applications), and their "Agent Society" proposes a communication protocol that enables agents to seamlessly provide services to each other. In addition, @StoryProtocol announced an agent communication protocol focused on intellectual property (IP), allowing tokenization, monetization, and purchase and sale transactions of IP between agents.

At the same time, we’ve also seen the rise of the following narrative frameworks:

• On-chain trading agents : Originally launched by @Spectral_Labs , their Syntax v2 ​​allows users to create agents capable of trading on the @HyperliquidX platform. However, its development was temporarily hampered by the emergence of a small vulnerability. Another agent worthy of attention is @BigTonyXBT , which uses the machine learning price prediction model provided by @AlloraNetwork to autonomously trade mainstream assets.

• Invest in DAO : Initially led by @ai16zdao , more DAOs began to emerge, such as @cryptohayesai and @AimonicaBrands . The core model of this type of DAO is to raise funds (such as SOL) through @daosdotfun (or other platforms), and then use these funds to conduct investment transactions to obtain profits. If the DAO's name is associated with a well-known crypto venture capital or public figure, it can also attract more attention.

• DeFi ****Agent : Represented by @modenetwork , become the leader of the DeFi agent ecosystem. Main application scenarios include AI-driven stablecoin revenue mining, liquidity provision (LPing), lending, etc. There are also many great teams in the ecosystem, such as @gizatechxyz , @autonolas , @BrianknowsAI , @SturdyFinance and @QuillAI_Network .

• AI App Store : @alchemistAIapp is a leader in this space by providing a no-code tool that allows users to easily create apps. Another platform @myshell_ai has a larger community of creators and developers, as well as more users, especially in the Web2 scene.

• Abstraction Layer : Led by @griffaindotcom and @orbitcryptoai , it provides an abstract experience that simplifies on-chain interactions. Through a simple and intuitive interface, it is especially suitable for ordinary users to easily use on-chain encryption services.

• Other narratives : such as the on-chain puzzle provided by @freysa_ai , the agent cracking bounty by @jailbreakme_xyz , the AI ​​security solution by @h4ck_terminal , and the unique agent model proposed by @god and @s8n - simulating the relationship between God and Satan debate.

Some agents focusing on Alpha analysis are gradually attracting attention, such as @unit00x0 (quantitative analyst), @kwantxbt (technical analyst) and @NikitaAIBase (comprehensive Alpha analyst).

Additionally, @sekoia_virtuals is emerging as a “quality assurance” agency for top projects. They only invest in three top projects and set strict standards, setting a new benchmark for on-chain venture capital (VC).

As a meme project, #Fartcoin unexpectedly went mainstream. Not only was it featured on Stephen Colbert’s show, it also exceeded $1 billion in market value. This shows that AI memes have become a cultural phenomenon.

About data and framework :

• @cookiedotfun is currently the preferred platform for on-chain data and social indicators in the field of AI agents. It is widely used to track market popularity, market value and agent performance.

• @getmasafi **** Integrate with @virtuals_io to provide real-time data support for agents to achieve self-learning and optimization.

• $TAOCAT is the first virtual agent powered by the Bittensor subnet, demonstrating the potential of real-time data. When the market generally fell, it became the only smart token that bucked the trend and rose sharply.

• @AgentTankLive provides a framework that allows agents to run entirely on computers, enabling more interesting Internet interactions while providing entertaining commentary.

Other new frameworks :

• The Rust-based RIG framework launched by @arcdotfun has quickly become popular due to its flexibility and versatility.

• @dolion_ai evolved from @dolos_diary into a toolkit for creating unique agents.

Summary and inspiration :

1. Strategies of top teams : Teams valued at over $50 million typically develop their own fine-tuned models and demonstrate their uniqueness and practical application through agents. Later, they will launch a no-code framework to allow more developers to easily create similar agents. This strategy not only increases the value of the agent, but also has a positive impact on the token price. If resources are limited, ideas can be implemented quickly based on existing frameworks such as Virtuals GAME or ai16z Eliza, but joining these communities can also help with access to distribution and marketing resources, as they currently have the highest industry visibility.

2. Investment strategy : Investing in agents with autonomous frameworks, or investing in the agent ecosystem/framework itself, often has a higher risk-reward ratio. A successful framework can not only attract users to pay, but also promote the growth of the value of framework-related tokens. For example, @arcdotfun 's Rust framework is a typical case.

3. On-chain and DeFi use cases : The most valuable AI use cases currently include:

1. Abstraction layer to help users use on-chain services more easily; 

2. Alpha agent that provides high-quality investment information; 

3. Execution agents that simplify trading, mining and lending operations; 

4. In the future, there may be intelligent agents that combine Alpha discovery and transaction execution capabilities. But the implementation of these use cases requires complete infrastructure support (discussed in detail in Part 2).

4. The importance of data : Data is the core of the agent, and high-quality data determines the output quality of the agent. Platforms like @cookiedotfun provide important data support to the industry, while @withvana tokenizes data through the DataDAO model, builds a data liquidity pool, and jointly promotes the progress of AI agents.

Part Two - Outlook to 2025

In the first part, we reviewed the development process of AI agents in 2024 and discussed the landmark innovations and breakthroughs during this year.

Now, in part two, we look ahead to 2025—a year when AI agents will not only become more useful, but will redefine our understanding of autonomy, intelligence, and collaboration.

Paving the way to 2025

Before looking to the future, it is necessary to mention that @virtuals_io will continue to solidify its position as the preferred distribution network for AI agents on the Base platform. Virtuals has become the core platform for smart agent projects. By binding liquidity, smart agents can not only gain higher exposure, but also establish in-depth cooperation with other high-quality projects. Currently, the total market value of Virtuals agents has reached US$3 billion, accounting for 77% of the entire AI agent market (source: @cookiedotfun ).

This trend will continue as more unique agents emerge on Virtuals, including:

• @aixbt_agent

• @luna_virtuals

• @sekoia_virtuals

• @VaderResearch

• @taocat_agent

• @Agent_YP

• @Gekko_Agent (recently launched by @getaxal )

• @SamIsMoving (focused on robotics research)

These diverse use cases will attract more developers, regardless of whether they already have tokens, to choose to launch projects on the Virtuals platform. And this growth will further drive the value of $VIRTUAL up.

So what about @ai16zdao and Eliza frameworks?

Although ai16zdao has led open source innovation with its Eliza framework, it currently lacks a launch platform, and the value accumulation of its token economic model is not as strong as Virtuals. However, there is still a lot of potential for the future. A dedicated team has been established to optimize its token economic model. If a launch platform is launched in the future, ai16zdao may become the preferred distribution platform on Solana, even surpassing existing competitors.

In 2025, we will also see significant upgrades to those top agents that already have product-market fit (PMF). For example, @aixbt_agent, as the leader in the field of conversational agents focusing on Alpha information, will further consolidate its position through more accurate answers and more insightful analysis.

This upgrade trend will occur throughout the entire ecosystem, and leaders in each field will stand out with their specialization and innovation.

Looking ahead to 2025

2025 will be the year of professionalization of AI agents. Leaders in each field will emerge, and each agent will dominate its niche:

• 3D models : Agents that provide high-quality visual design for games and AR/VR.

• Speech module : an agent that implements natural and emotional human speech.

• Personalized interaction : an agent with a unique, human-like conversational style.

• Streaming agents : Interactive agents that perform well on platforms like Twitch and YouTube.

• Automated trading agent : an agent that can continuously execute profitable transactions.

• DeFi -focused agents : agents that optimize yield strategies, lending and liquidity provision.

• Abstracted agents : agents that simplify on-chain interactions through user-friendly interfaces.

Just as humans are diverse and specialized, AI agents will become equally diverse. The uniqueness of each agent will be closely related to its underlying model, data and infrastructure. However, the success of the entire ecosystem will depend on a strong decentralized AI infrastructure.

The role of decentralized AI infrastructure

In order for AI agents to achieve scale in 2025, decentralized infrastructure is crucial. Without it, industries may face performance bottlenecks, lack of transparency, and limited innovation.

Here’s a look at the importance of decentralized infrastructure and the solutions currently being developed:

1. Verifiability

Trust is the cornerstone of decentralized AI. As AI agents become more autonomous, we need systems that can verify their operating mechanisms. For example:

• Is this "agent" a real AI, or is it pretending to be a human?

• Is the output generated by the claimed algorithm or model?

• Are the calculations correct and safe?

This also involves Trusted Execution Environments (TEEs), which ensure that the computing process is protected from external interference by running calculations in trusted hardware. At the same time, technologies such as Zero-Knowledge Proofs (ZKPs) will also play an important role. These techniques allow agents to prove the accuracy and reliability of their outputs while protecting the privacy of the underlying data.

Well-known projects

• @OraProtocol : Exploring the infrastructure of secure AI, but its token economic model still needs to be optimized.

• @hyperbolic_labs : The first to propose the "Proof-of-Sampling" technology to verify the calculation and reasoning process of AI.

• @PhalaNetwork : Known for its Trusted Execution Environment (TEE) infrastructure, which provides additional security for decentralized AI.

2. payment system

In order for AI agents to operate autonomously in the real world, they need a complete payment system. These systems must not only support the conversion of legal currency to digital currency (on/off-ramping), but also handle transactions between agents, service exchange, and financial management in operations.

Imagine that agents can independently manage their own finances, purchase computing resources, and even exchange services with other agents - this will become the core foundation of agent-to-agent commerce.

Well-known protocols

• @crossmint : Provide payment tools for AI to simplify transaction processes.

• @Nevermined_io : Supports business interaction and service exchange between agents.

• @trySkyfire : Focus on intelligent payment and financial management.

3. decentralized computing

AI’s demand for computing resources is growing at an alarming rate—almost doubling every 100 days. Traditional centralized cloud services (such as AWS) are difficult to meet this demand due to high costs and limited scalability. Decentralized computing networks provide a solution to this problem by allowing anyone with idle resources to join the network, provide computing power, and obtain rewards.

GPU-based debt financing models have even emerged this year (such as @gaib_ai ) to help data centers finance and scale their operations. This model lowers the entry barrier, enables more people to participate in the decentralized computing network, and provides broader computing support for AI.

Well-known protocols

• @AethirCloud : A decentralized computing network specially built for AI and Web3.

• @ionet : Provide scalable computing solutions to meet the growing workload demands of AI.

4. data

If AI is the brain, then data is the oxygen it needs to survive. The quality, reliability and completeness of data directly determine the performance of the AI ​​model. However, obtaining and labeling high-quality data is expensive, and poor-quality data can seriously affect model performance.

What’s exciting is that some platforms are giving users ownership of their data and allowing them to monetize it. For example, @withvana allows users to tokenize data and trade it through Data Liquidity Pools (DLPs). Imagine that you can choose to join a TikTok data DAO or Reddit data DAO and convert your data contribution into revenue. This model not only gives users more power, but also provides a steady stream of high-quality data for the development of AI.

Well-known protocols

• @cookiedotfun : Provide credible data indicators and insights to support intelligent agent decision-making.

• @withvana : Promoting the development of the data economy by tokenizing user data and trading it in a decentralized market.

• @getmasafi : Partnering with @virtuals_io to build the world's largest decentralized AI data network to support dynamic and adaptive agents.

5. Model creators and marketplaces

2025 will see the emergence of a large number of new AI agents, many of which will be driven by decentralized models. Not only will these models be more advanced, they will also have human-like reasoning capabilities, memory capabilities, and even "cost awareness."

For example, @NousResearch is developing a "starvation" mechanism to introduce economic constraints to AI models. If an agent cannot pay the cost of inference, it will become inoperable (i.e., "die"), prompting the agent to learn to prioritize tasks more efficiently.

Well-known projects

• @NousResearch : By introducing a "hunger" mechanism, AI agents are taught how to manage resources.

• @PondGNN : with @virtuals_io **** Collaborate to provide tools for the creation and training of decentralized models.

• @BagelOpenAI : Utilize fully homomorphic encryption (FHE) and Trusted Execution Environments (TEEs) to provide privacy-preserving infrastructure.

6. Distributed training and federated learning

As AI models become increasingly larger and more complex, centralized training systems can no longer meet demand. Distributed training makes the training process faster and more efficient by spreading the workload across multiple decentralized nodes. At the same time, Federated Learning allows multiple organizations to collaboratively train models without sharing original data, thus solving privacy issues.

For example, @flock_io provides a secure decentralized platform that connects AI engineers, model proposers, and data providers to create a marketplace for model training, validation, and deployment. The platform supports projects such as @AimonicaBrands and drives the development of many other innovative models.

Well-known projects

• @flock_io : "Uber of AI", building a decentralized AI model training and deployment ecosystem by connecting multiple resources.

7. Swarm intelligence and coordination layer

As the AI ​​agent ecosystem continues to grow, seamless collaboration between agents becomes critical. Swarm Intelligence allows multiple agents to work together and integrate their capabilities to achieve a common goal. The coordination layer simplifies cooperation between agents by abstracting complexity.

For example, @TheoriqAI **** Use a meta-agent to identify the most suitable agent for a certain task and form a "group" to complete the target task. The platform also ensures task quality and assignment of responsibilities by tracking the reputation and contributions of agents.

Well-known projects

• @joinFXN : Develop unified communication and business protocols to simplify agent interaction.

• @virtuals_io : Supports interaction and integration between agents and promotes ecological development.

• @TheoriqAI : Develop advanced coordination tools, including swarm intelligence formation and task allocation mechanisms.

Why decentralized infrastructure is crucial

The next stage of development of AI agents is highly dependent on infrastructure. Without verifiability, payment systems, scalable computing power, and robust data pipelines, the entire ecosystem could stagnate. Decentralized infrastructure solves these problems by:

• Trust and Transparency : Ensure the security and verifiability of agents and their outputs.

• Scalability : Meet AI’s growing demands for computing and data.

• Collaboration capabilities : Through the swarm intelligence and coordination layer, intelligent agents can collaborate seamlessly.

• Empowerment : Through data ownership and decentralized tools, users and developers can shape the future of AI without centralized control.

Other trends worth watching

There are also narrative themes worth watching in 2025, which I’ll go into more detail later:

• Agent Metaverse/ AI & Games : Projects like @realisworlds and @ARCAgents are combining agents with games and immersive virtual worlds to create new interactive experiences.

• On-chain and DeFi tools : Protocols like @Almanak__ , @AIWayfinder , @getaxal , @Cod3xOrg , @griffaindotcom , and @orbitcryptoai are building important tools for DeFi-driven agents and promoting application scenarios for on-chain agents.

in conclusion

2025 will be an important turning point in the development of AI agents. This year we will witness their rapid progress towards artificial general intelligence (AGI, Artificial General Intelligence) with the ability to perceive. These agents will no longer be limited to completing a single task, but will be able to conduct transactions autonomously, collaborate with other agents, and even interact with humans in ways beyond our imagination.

Imagine an agent that can analyze market data, complete transactions, manage finances, and even cooperate with other agents to complete complex tasks. They will be deeply integrated into our daily lives, from on-chain decentralized finance (DeFi) operations to various interactions in the real world, demonstrating unprecedented levels of autonomy and intelligence.

The realization of all this is inseparable from the decentralized infrastructure currently being built - including verifiable systems, payment tools, computing networks and coordination layers between agents. These technologies will lay a solid foundation for the future of the intelligent agent ecosystem. For developers, investors, and technology enthusiasts, there has never been a better time to join this space and shape the future.

2025 is not only the continuation of the development of existing technologies, but also the beginning of a new era of AI agents, marking the dawn of a new intelligent ecosystem.

Disclaimer

This document is for reference and entertainment purposes only. The views expressed in this article do not constitute investment advice or recommendations. Readers should conduct sufficient due diligence based on their own financial situation, investment objectives and risk tolerance before making any investment (these factors are not considered in this document). This document does not constitute an offer to buy or a solicitation of an invitation to buy or sell any assets mentioned in it.