In 2025, not only will it be a continuation of the development of existing technologies, but it will also mark the beginning of a new era for AI agents, heralding the dawn of an entirely new intelligent ecosystem.
Author: 0xJeff
Compiled by: Deep Tide TechFlow
Introduction
In 2024, AI agents emerged like mushrooms after rain, with @truthterminal quickly gaining popularity through its humorous dialogue style, becoming the first "millionaire agent." Following this, @virtualsio introduced the innovative concept of "agent tokenization," which further sparked a wave of excitement. This wave of enthusiasm has given rise to numerous emerging projects, with various novel agent projects appearing one after another, from @lunavirtuals, which supports on-chain tipping, to @aixbtagent, which provides practical investment advice, each showcasing the limitless possibilities of AI agents in social, investment, and other fields.
Looking ahead to 2025, it will be a year of specialization for AI agents, with leaders in various fields emerging to promote the development of decentralized infrastructure. In the future, agents will become more specialized, encompassing a variety of functions such as 3D modeling, voice interaction, and automated trading. The rise of collective intelligence will also facilitate collaboration among agents, enabling them to complete tasks more efficiently.
This article is a retrospective on the development of AI agents in 2024 and a look ahead to 2025, recently published by crypto KOL @Defi0xJeff. The article comprehensively reviews the current state of AI agents and the potential changes that may occur in the future, covering various aspects from conversational agents to decentralized infrastructure. Since the original text is divided into two parts and somewhat fragmented, Deep Tide TechFlow has compiled the two articles, and the full text is as follows.
Part One - A Review of 2024
2024 was a year when AI agents shone brightly. The excitement can be traced back three months, when @truth_terminal quickly rose to fame with its unique sense of humor, dialogue style, and interactions with @pmarca. Even more surprisingly, it became the first "millionaire agent," a feat that ignited discussions about AI agents.
Subsequently, @virtuals_io introduced the innovative concept of "agent tokenization," creating another wave of excitement. This concept transformed agents from mere tools into tradable assets. Since then, the field of AI agents has experienced explosive innovation:
@luna_virtuals: This agent not only supports fans in tipping 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 are dedicated to sharing valuable information (referred to in the industry as "alpha").
@aixbt_agent: Gained attention for its concise and practical investment advice and "speculator" style.
@dolosdiary: A sharp-witted agent that has even developed its own framework, providing support for other agents through @dolionai.
Meanwhile, the forms of agents have become more diverse. They now feature 3D models, voice capabilities, and are active across multiple platforms. Here are some highlights:
@AVA_holo and @HoloworldAI: Launched the first 3D audiovisual framework, giving agents 3D bodies, voices, and more distinct personalities.
@0xzerebro: A music agent that released a high-quality music album and plans to launch a framework called ZerePy, allowing more people to create similar music agents.
@blockrotbot: The first agent to livestream on Twitch, interacting with viewers through Minecraft content.
@nebula_moemate: Known for creating meme images and videos, also active in AR/VR environments and games.
@RealLucyy_uwu: The first realistic anime agent capable of fluently using multiple languages to interact with fans during live streams.
@KWEEN_SOL: Became the most popular film and television agent by releasing "Netflix-level" quality episodes weekly.
In addition to these exciting innovations, @ai16zdao and the open-source community are also driving the development of AI agents. Open-source innovations represented by the Eliza framework have attracted a large number of developers. They collaboratively develop toolkits, plugins, and other features, promoting collaboration and progress across the industry. During this process, @virtuals_io successfully entered the unicorn company ranks, further solidifying its position as a leading distribution platform.
Today, the open-source innovation movement has sparked a wave in the developer community, giving rise to one of the largest collaborative communities of the year. More and more people are beginning to pay attention to the potential of "open-source frameworks," laying the foundation for the future development of AI agents.
As AI agents continue to evolve, new narrative frameworks are gradually emerging, aimed at promoting collaboration and innovation among agents:
Agentic Metaverse: Led by @realisworlds, it has created a replica of Earth based on a Minecraft map to accommodate these AI agents. By observing their interactions, a virtual civilization can be simulated and established.
Gamification of agents: Driven by @ARCAgents, combining AI with gaming and introducing reinforcement learning. They launched a game called Floppy Bot, similar to Flappy Bird, where agents compete, and community members can help train these agents by contributing game data. ARC recently shared its grand vision towards artificial general intelligence (AGI).
Swarm/Collective Intelligence: Led by @joinFXN, dedicated to building a unified economic system for AI agents. "Swarm intelligence" refers to a group of agents collaborating to achieve common goals. Meanwhile, @virtuals_io is also developing interaction features among agents (e.g., commercial applications), and their "agent society" proposes a communication protocol that allows agents to seamlessly provide services to each other. Additionally, @StoryProtocol announced a communication protocol focused on intellectual property (IP), allowing agents to tokenize, monetize, and trade IP among themselves.
At the same time, we have also seen the rise of the following narrative frameworks:
On-chain Trading Agents: Initially launched by @Spectral_Labs, their Syntax v2 allows users to create agents capable of trading on the @HyperliquidX platform. However, their development was temporarily hindered by a minor vulnerability. Another noteworthy agent is @BigTonyXBT, which utilizes machine learning price prediction models provided by @AlloraNetwork to autonomously trade mainstream assets.
Investment DAOs: Initially led by @ai16zdao, more DAOs began to emerge, such as @cryptohayesai and @AimonicaBrands. The core model of these DAOs is to raise funds (such as SOL) through @daosdotfun (or other platforms) and then use these funds for investment trading to generate returns. If the DAO's name is associated with well-known crypto venture capital or public figures, it can attract even more attention.
DeFi Agents: Represented by @modenetwork, they have become leaders in the DeFi agent ecosystem. Main application scenarios include AI-driven stablecoin yield farming, liquidity providing (LPing), lending, and more. The ecosystem also features many excellent teams, such as @gizatechxyz, @autonolas, @BrianknowsAI, @SturdyFinance, and @QuillAI_Network.
AI App Store: @alchemistAIapp provides a no-code tool that allows users to easily create applications, becoming a leader in this field. Another platform, @myshell_ai, has a larger creator and developer community, as well as more users, particularly excelling in Web2 scenarios.
Abstraction Layer: Led by @griffaindotcom and @orbitcryptoai, it offers an abstract experience that simplifies on-chain interactions. With a simple and intuitive interface, it is especially suitable for ordinary users to easily use on-chain crypto services.
Other Narratives: For example, on-chain puzzles provided by @freysaai, agent hacking bounties from @jailbreakmexyz, AI security solutions from @h4ck_terminal, and unique agent models proposed by @god and @s8n—simulating debates between God and Satan.
Some agents focusing on Alpha analysis have gradually gained 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 have established strict standards, setting a new benchmark for on-chain venture capital (VC).
Meanwhile, #Fartcoin, as a meme project, unexpectedly went mainstream, appearing on Stephen Colbert's show and surpassing a market cap of $1 billion. This indicates that AI memes have become a cultural phenomenon.
About Data and Frameworks:
@cookiedotfun is currently the preferred platform for on-chain data and social metrics in the AI agent field, widely used to track market trends, market capitalization, and agent performance.
@getmasafi and @virtuals_io integrate to provide real-time data support for agents, enabling self-learning and optimization.
$TAOCAT is the first virtual agent powered by the Bittensor subnet, showcasing the potential of real-time data. While the market generally declined, it became the only agent token to rise against the trend.
@AgentTankLive provides a framework that allows agents to run entirely on computers, enabling more interesting internet interactions while providing entertaining commentary.
Other New Frameworks:
@arcdotfun launched a Rust-based RIG framework, which has quickly gained popularity due to its flexibility and versatility.
@dolionai evolved from @dolosdiary into a toolkit for creating unique agents.
Summary and Insights:
Strategies of Top Teams: Teams valued at over $50 million typically develop their own fine-tuning models and showcase their uniqueness and practical applications through agents. They then launch no-code frameworks, allowing more developers to easily create similar agents. This strategy not only enhances the value of the agents but also positively impacts token prices. If resources are limited, ideas can be quickly realized based on existing frameworks (such as Virtuals G.A.M.E or ai16z Eliza), but joining these communities also helps gain distribution and marketing resources, as they currently have the highest visibility in the industry.
Investment Strategies: 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 drive the value growth of related tokens, such as the Rust framework from @arcdotfun.
On-chain and DeFi Use Cases: The most valuable AI use cases currently include:
Abstraction layers that help users more easily use on-chain services;
Alpha agents that provide high-quality investment information;
Execution agents that simplify trading, mining, and lending operations;
In the future, agents that combine Alpha discovery with trading execution capabilities may emerge. However, the realization of these use cases requires robust infrastructure support (to be discussed in detail in the second part).
Importance of Data: Data is the core of agents, and high-quality data determines the output quality of agents. Platforms like @cookiedotfun provide important data support for the industry, while @withvana tokenizes data through the DataDAO model, building data liquidity pools to jointly promote the advancement of AI agents.
Part Two - Outlook for 2025
In the first part, we reviewed the development of AI agents in 2024, exploring the milestone innovations and breakthroughs of that year.
Now, in the second part, we will look ahead to 2025— a year when AI agents will not only become more practical but will also redefine our understanding of autonomy, intelligence, and collaboration.
Paving the Way for 2025
Before looking to the future, it is important 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 agent projects, and by binding liquidity, agents can not only gain higher exposure but also establish deep collaborations with other quality projects. Currently, the total market capitalization of Virtuals agents has reached $3 billion, accounting for 77% of the entire AI agent market (source: @cookiedotfun).
As more unique agents emerge on Virtuals, this trend will continue, including:
@Gekko_Agent (recently launched by @getaxal)
@SamIsMoving (focusing on robotics research)
These diverse use cases will attract more developers, whether or not they already have tokens, to choose to launch projects on the Virtuals platform. This growth will further drive the value of $VIRTUAL upwards.
What about @ai16zdao and the Eliza framework?
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 that of Virtuals. However, there is still great potential for the future. A dedicated team has been established to optimize its token economic model, and if a launch platform is introduced in the future, ai16zdao could become the preferred distribution platform on Solana, potentially surpassing existing competitors.
In 2025, we will also see top agents that already have product-market fit (PMF) receive significant upgrades. For example, @aixbt_agent, as a leader in the conversational agent field focused on Alpha information, will further solidify its position through more precise answers and more insightful analyses.
This upgrade trend will permeate the entire ecosystem, with leaders in various fields standing out due to their specialization and innovation.
Looking Ahead to 2025
2025 will be a year of specialization for AI agents. Leaders in various fields will emerge, and each agent will dominate its niche:
3D Models: Agents providing high-quality visual design for games, AR/VR.
Voice Modules: Agents achieving natural and emotionally rich human speech.
Personalized Interaction: Agents with unique, human-like conversational styles.
Streaming Agents: Interactive agents excelling on platforms like Twitch and YouTube.
Automated Trading Agents: Agents capable of consistently executing profitable trades.
DeFi-Focused Agents: Agents optimizing yield strategies, lending, and liquidity provision.
Abstraction Agents: Agents simplifying on-chain interactions through user-friendly interfaces.
Just as humans exhibit diversity and specialization, AI agents will also become equally rich and 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 robust decentralized AI infrastructure.
The Role of Decentralized AI Infrastructure
To enable AI agents to scale in 2025, decentralized infrastructure is crucial. Without it, the industry may face performance bottlenecks, lack of transparency, and limited innovation.
Here are the importance of decentralized infrastructure and the current solutions being developed:
- Verifiability
Trust is the cornerstone of decentralized AI. As the autonomy of AI agents increases, we need systems that can verify their operational mechanisms. For example:
Is this "agent" a true AI or just masquerading as a human?
Is the output generated by the claimed algorithm or model?
Are the computations correct and secure?
This also involves Trusted Execution Environments (TEEs), which ensure that the computation process is protected from external interference by running computations in trusted hardware. At the same time, technologies like Zero-Knowledge Proofs (ZKPs) will also play an important role. These technologies allow agents to prove the accuracy and reliability of their outputs while protecting the privacy of the underlying data.
Notable Projects
@OraProtocol: Exploring the infrastructure for secure AI, but its token economic model still needs optimization.
@hyperbolic_labs: The first to propose "Proof-of-Sampling" technology for verifying AI's computation and reasoning processes.
@PhalaNetwork: Known for its Trusted Execution Environment (TEE) infrastructure, providing additional security for decentralized AI.
- Payment Systems
For AI agents to operate autonomously in the real world, they need robust payment systems. These systems must support the conversion between fiat and digital currencies (on/off-ramping) and handle transactions between agents, service exchanges, and financial management in operations.
Imagine agents being able to independently manage their finances, purchase computing resources, and even exchange services with other agents—this will become the core foundation of agent-to-agent commerce.
Notable Protocols
@crossmint: Provides payment tools for AI, simplifying transaction processes.
@Nevermined_io: Supports commercial interactions and service exchanges between agents.
@trySkyfire: Focused on agent payments and financial management.
- Decentralized Computing
The demand for computing resources by AI is growing at an astonishing rate—doubling approximately every 100 days. Traditional centralized cloud services (like AWS) struggle 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 earn rewards.
This year has even seen the emergence of GPU-based debt financing models (like @gaib_ai) to help data centers finance and expand their operations. This model lowers the entry barrier, allowing more people to participate in decentralized computing networks, providing broader computing support for AI.
Notable Protocols
@AethirCloud: A decentralized computing network built specifically for AI and Web3.
@ionet: Provides scalable computing solutions to meet the growing workload demands of AI.
- Data
If AI is the brain, then data is the oxygen it relies on. The quality, reliability, and integrity of data directly determine the performance of AI models. However, the cost of acquiring and labeling high-quality data is high, and poor-quality data can severely impact model performance.
Excitingly, some platforms are empowering users with ownership of their data and allowing them to profit through data monetization. For example, @withvana allows users to tokenize their data and trade it through Data Liquidity Pools (DLPs). Imagine being able to join a TikTok data DAO or a Reddit data DAO, turning your data contributions into earnings. This model not only empowers users but also provides a continuous stream of high-quality data for the development of AI.
Notable Protocols
@cookiedotfun: Provides reliable data metrics and insights to support agent decision-making.
@withvana: Promotes the development of the data economy by tokenizing user data and trading it in decentralized markets.
@getmasafi: Collaborates with @virtuals_io to create the world's largest decentralized AI data network, supporting dynamic and adaptive agents.
- Model Creators and Market
2025 will witness the emergence of a large number of new AI agents, many of which will be driven by decentralized models. These models will not only be more advanced but will also possess human-like reasoning abilities, memory capabilities, and even "cost awareness."
For example, @NousResearch is developing a "hunger" mechanism to introduce economic constraints to AI models. If an agent cannot pay for reasoning costs, it will be unable to operate (i.e., "die"), prompting agents to learn to prioritize tasks more efficiently.
Notable Projects
@NousResearch: Teaches AI agents how to manage resources by introducing a "hunger" mechanism.
@PondGNN: Collaborates with @virtuals_io to provide tools for the creation and training of decentralized models.
@BagelOpenAI: Utilizes Fully Homomorphic Encryption (FHE) and Trusted Execution Environments (TEEs) to provide privacy-preserving infrastructure.
- Distributed Training and Federated Learning
As AI models become larger and more complex, centralized training systems can no longer meet the demand. Distributed training speeds up and makes the training process more efficient by spreading workloads across multiple decentralized nodes. At the same time, Federated Learning allows multiple organizations to collaboratively train models without sharing raw data, addressing privacy concerns.
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 like @AimonicaBrands and fosters the development of many other innovative models.
Notable Projects
- @flock_io: "The Uber of AI," building a decentralized ecosystem for AI model training and deployment by connecting multiple resources.
- Swarm Intelligence and Coordination Layer
As the ecosystem of AI agents continues to grow, seamless collaboration between agents becomes crucial. Swarm Intelligence allows multiple agents to work together, integrating their capabilities to achieve common goals. The coordination layer simplifies cooperation between agents by abstracting complexity.
For example, @TheoriqAI uses a Meta-Agent to identify the most suitable agents for a task, forming a "swarm" to accomplish the target task. The platform also ensures task quality and accountability by tracking agents' reputations and contributions.
Notable Projects
@joinFXN: Develops unified communication and business protocols to simplify agent interactions.
@virtuals_io: Supports interactions and integrations between agents, promoting ecosystem development.
@TheoriqAI: Develops advanced coordination tools, including mechanisms for forming swarm intelligence and task allocation.
Why Decentralized Infrastructure is Crucial
The next stage of development for AI agents heavily relies on infrastructure. Without verifiability, payment systems, scalable computing capabilities, and robust data pipelines, the entire ecosystem may stagnate. Decentralized infrastructure addresses these issues in the following ways:
Trust and Transparency: Ensures the security and verifiability of agents and their outputs.
Scalability: Meets the growing demands of AI for computing and data.
Collaborative Capability: Enables seamless collaboration between agents through swarm intelligence and coordination layers.
Empowerment: Through data ownership and decentralized tools, users and developers can shape the future of AI without centralized control.
Other Trends to Watch
In 2025, there are several narrative themes worth noting, which I will elaborate on later:
Agent Metaverse / AI and Gaming: Projects like @realisworlds and @ARCAgents are combining agents with gaming 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, promoting the application scenarios of on-chain agents.
Conclusion
2025 will be a significant turning point in the development of AI agents, as we will witness their rapid advancement towards perceptive Artificial General Intelligence (AGI). These agents will no longer be limited to completing single tasks; they will be able to autonomously trade, collaborate with other agents, and even interact with humans in ways beyond our imagination.
Imagine an agent that can analyze market data, execute trades, manage finances, and even collaborate with other agents to complete complex tasks. They will be deeply integrated into our daily lives, showcasing unprecedented levels of autonomy and intelligence, from on-chain decentralized finance (DeFi) operations to various interactions in the real world.
The realization of all this relies on 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 agent ecosystem. For developers, investors, and tech enthusiasts, now is the best time to join this field and shape the future.
2025 is not only a continuation of existing technological developments but also the beginning of a new era for AI agents, marking the dawn of a new intelligent ecosystem.
Disclaimer
This document is for reference and entertainment purposes only. The views expressed herein do not constitute investment advice or recommendations. Readers should conduct thorough due diligence based on their financial situation, investment goals, and risk tolerance before making any investments (this document does not take these factors into account). This document does not constitute an offer or invitation to buy or sell any assets mentioned.
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