The increase of AI-driven Crypto agents follows a familiar trajectory that reflects the initial boom, bust and resurrection of ICO -ra projects. As the Blockchain’s early adventures have evolved in the hype before being considered in the sustainable ecosystem, the current wave of AI agent projects undergo rapid market changes.
A new report of HTX ventures and HTX research says investors are growing carefully as competition in the sector intensifies, liquidity has spread and many projects struggle to define clear cases of use. However, as the sector moves beyond its imagination -that stage, AI-driven Crypto agents is expected to change sustainable business models supported by the real utility.
From meme hype to the fact: the evolution of crypto agents
The initial wave of crypto agent projects in 2024 was driven by accidental enthusiasm for AI projects. Following the effect of A $ 50,000 Bitcoin donation From Marc Andreessen in October 2024 and the success of the token launchpads earlier in the year, many AI agent projects entered the Q1 space of 2024 and rapidly melt liquidity through the Q1 of 2025. As any emerging sector, the early stage of hype was not always translated into long-term flexion, You have a sector agent followed.
The market segment is now entering a more mature phase, and the focus is moving from the speculation -awareness to the generation of revenue and product performance. The winners of this emerging scene are the ones that can generate stable income, cover the operations of running AI models and provide tangible value to users and investors.
AI agent applications emphasize the implementation of real-world and commercialization of this technology, especially in areas such as automatic tradeAsset management, market review and crosschain contact. This procedure is aligned with multi-aging systems and Defai (Decentralized Finance + AI) initiatives such as Hey anon, Griffain and Caingpt.
Recent research highlights The advantages of multi-agent systems (MAS) in portfolio management, especially in cryptocurrency investments. Projects such as Griffain, Neur, and Buzz have shown how AI can help users to interact with defi protocols and make informed decisions. Unlike single-agent AI models, multi-agent systems use cooperation with specialized agents to enhance market review and implementation. These agents work with teams, such as data analysts, risk reviewers and trade implementation units, each trained to handle specific tasks.
More frameworks also introduced inter-agent communication mechanisms, where agents within the same team refinement predictions through collective study, reducing markets in market trend. The next stage of defai is likely to be involved in the deeper integration of decentralized management models, where multi-agencies participate in protocol management, optimization of treasury and implementation of onchain compliance.
DeepSeek-R1: A breakthrough in AI agent training
A success in AI agent technology has come DeepSeek-R1a change that challenges the traditional AI training method. Unlike previous models, which rely on administered fine tuning (SFT) followed by a reinforcement study (RL), DeepSeek-R1 takes a different approach, optimized in full by studying reinforcement without pre-administered stage. This shift has led to noticeable improvements in reasoning and flexibility capabilities, which puts ways for more sophisticated crypto agents driven by AI.
To understand the shift of this paradigm, consider two different study methods. In the traditional SFT and RL models, a first student learning from a workbook, training problems with answer sets (SFT), and then receiving instruction to refine their understanding (RL). In contrast, in the Deepseek-R1 model (pure reinforcement study), the student is thrown directly into a test and learns through trial and mistake. This method allows the student to improve the dynamic based on feedback rather than relying on predetermined answers.
The seizure of the pure RL model of the DeepSeek-R1, AI agents learn by trial and mistake in real-world conditions, dynamically adjusting their techniques based on immediate comment.
This method allows for greater flexibility, making it useful for multi-age-aged AI systems at the DeFI, where real-time market fluctuations require agents to make autonomous, data-driven decisions. For example, AI-powered agents can monitor pools pools, see arbitration opportunities and optimize asset allocations based on real-time market conditions. These agents quickly adapt to market change, ensuring better capital expansion.
Launched in late November 2024, Idegen is the first Crypto Ai agent Built on DeepSeek R1. The integration of the R1 model of DeepSeek Emphasizes how crypto AI agents can inherit enhanced reasoning capabilities, competing with other established AI models at a part of the cost.
This change toward RL-powered, multi-agent AI in Defi Automation underscores why closed-source AI models (such as Openai GPT-based systems) becomes an unstable cost. In workflows that often require processing of 10,000+ tokens per transaction, closed AI models impose significant computational costs, limiting scalability. In contrast, open-source RL models such as Deepseek-R1 allow for decentralized, excellent AI development that is customized for Defi applications.
The future of AI agents on the web3
The key to longevity in this sector lies in ongoing change, flexibility and cost efficiency. Open-source AI models such as Deepseek-R1 lower entry barriers, allowing blockchain-native startups to develop specialized AI solutions. Meanwhile, meanwhile Advancement to Defai and multi-agent systems will bring long-term integration between AI and decentralized finances.
Takeaway is clear: projects should prove their value beyond the hype. Those who have sustainable economic models and seizures of AI advances will determine the future of intelligent blockchain ecosystems. The ICO period of crypto agents is emerging, and the next wave of winners is the one that may be a change in long -term probability.
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