Original | Odaily Planet Daily (@OdailyChina)

Author | Azuma (@azuma_eth)

The AI Agent sector continued to accelerate its decline today. Aside from some memes related to the DeepSeek concept, major tokens such as VIRTUAL, AI16Z, AIXBT, ARC, and other large and small tokens recorded double-digit declines.

• Odaily Note: Please refer to “Deepseek surpasses ChatGPT to top AppStore, which AI concept tokens are related?”.

The decline is certainly related to the overall weakening of the market, but looking around the market discussions, some investors attribute the collective decline of AI Agents this round to the explosive popularity of DeepSeek, believing that DeepSeek, as a new generation of more economical LLM, will impact and even reshape the current Crypto AI landscape.

I personally do not support this view.

The core point of the aforementioned doubts is — Will the iterative development of LLM disrupt existing Agents? Setting aside the debate over whether DeepSeek's current reasoning quality surpasses that of ChatGPT and Claude, if we assume from an economically sustainable perspective that DeepSeek has more promising iterative potential, will the iterated DeepSeek create a new batch of Agents, rendering the existing ones "yesterday's news"?

To answer this question, we need to first understand the relationship between LLM and Agent. We attempt to use DeepSeek itself and another major "domestic star" Kimi to address this question (to be honest, Kimi seems to have better quality in this regard).

DeepSeek:

The relationship between AI Agent (Artificial Intelligence Agent) and AI LLM (Large Language Model) is that LLM can serve as one of the core components of an Agent, especially when handling natural language understanding and generation tasks. An AI Agent is an intelligent system capable of autonomously executing tasks and interacting with the environment, while LLM provides the Agent with powerful language processing capabilities, enabling it to better understand and generate human language, thus playing a role in dialogue, recommendations, analysis, and other scenarios. In short, LLM is an important technical support for AI Agents to achieve intelligent language interaction.

Kimi:

• Agent typically relies on LLM to understand and generate natural language. LLM provides powerful language processing capabilities, allowing Agents to interact with users in natural language, understand user needs, and generate corresponding responses.

• Agents do not solely depend on LLM's language processing capabilities; they also integrate decision-making, problem-solving, interaction with the external environment, and execution of actions. LLM provides the foundation for understanding and generating language, while Agents build upon this foundation for task planning and execution.

• Agents can also combine other tools and APIs to expand their functionality. For example, Agents can call search engines, database query tools, file reading and writing tools, etc., to complete more complex tasks. LLM provides the language understanding and generation capabilities in this process, enabling AI Agents to better interact with these tools.

In summary, the operation of Agents requires the support of LLM, but the quality of their expression and interaction does not solely depend on LLM. In fact, it is the other capabilities beyond LLM that lead to significant differences among different Agents.

For example, the reason why AIXBT can "crush" other similar Agents in output is fundamentally due to its superior performance in prompt design, post-processing mechanisms, context management, fine-tuning strategies, randomness control, external tool integration, and user feedback mechanisms, allowing it to generate industry-relevant expressions — whether you call it a first-mover advantage or a moat, this is the current advantage of AIXBT.

Having understood this relational logic, we can now answer the core question posed earlier: “Will the iterative development of LLM disrupt existing Agents?”

The answer is no, because Agents can easily evolve by integrating the capabilities of the new generation of LLM through API integration, thereby improving interaction quality, enhancing efficiency, and expanding application scenarios… especially considering that DeepSeek itself provides an API format compatible with OpenAI.

In fact, quick-reacting Agents have already completed the integration of DeepSeek. Shaw, the founder of AI16Z, stated this morning that the AI Agent construction framework Eliza developed by AI16Z DAO completed support for DeepSeek two weeks ago.

Given the current trend, we can rationally assume that after AI16Z's Eliza, other major frameworks and Agents will also quickly complete the integration of DeepSeek. In this way, even if there will be some short-term impacts from new generation DeepSeek Agents, in the long run, the competition among various Agents will still depend on the external capabilities mentioned earlier, and at that time, the accumulation of development results brought by first-mover advantages will once again become apparent.

Finally, let’s share some comments from industry leaders regarding DeepSeek to recharge the faith of those who are steadfast in the AI Agent sector.

Frank, the founder of DeGods, stated yesterday: “People's thoughts on this matter (DeepSeek iterating the old market) are wrong. Current AI projects will benefit from new models like DeepSeek; they just need to replace OpenAI API calls with DeepSeek, and their output will improve overnight. New models will not disrupt Agents but will accelerate their development.”

Daniele, a trader focused on the AI sector, remarked: “If you are selling AI tokens because the DeepSeek model is cheap and open-source, you need to know that DeepSeek is actually very helpful in expanding AI applications to millions of users at a low entry price. This may be the best thing for the industry so far.”

Shaw also published a lengthy response to the impact of DeepSeek this morning, starting with the following sentence: “More powerful models are always good for Agents. For years, major AI labs have been surpassing each other. Sometimes Google leads, sometimes OpenAI, sometimes Claude, and today it’s DeepSeek…”

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