Hermes Agent登顶OpenRouter榜首：AI智能体步入多路线竞争时代

This article organizes the latest developments in the global AI agent industry, with key takeaways as follows:

1. Latest industry milestone: In May 2026, Hermes Agent, an open-source AI agent platform, topped OpenRouter's global application token consumption ranking with an average daily call volume of 291 billion tokens. Its weekly calls exceed 1.75 trillion tokens, and cumulative token consumption has broken through 6.37 trillion tokens, surpassing the long-time leader OpenClaw for the first time. Chinese models hold most seats in the core support tier of this ranking, reflecting deep changes in the global AI industry landscape.

2. Current overall industry landscape: AI agents have entered a mature stage of multi-path competition, with two clear development mainstreams that will eventually converge. The ultimate goal is to build task-solving AI with self-evolution, intelligent search, and automated execution capabilities. The AI industry is also shifting from a parameter race to a validation phase focused on real-world deployment and usage.

The AI agent industry has now entered the phase of deployment-focused competition, with key actionable insights for brands looking to lay out AI-related business and capture industry trends:

1. Changes in user demand and consumer trends: User demand for AI products has shifted from superficial Q&A to complex task execution. Users increasingly value AI's autonomous learning capability and data privacy security, and only products aligned with these demands can win high-frequency real usage. Brands need to adjust their AI layout strategies to fit this shifting demand.

2. Guidance for industry development direction: AI agents are evolving toward integrating self-evolution memory, intelligent search, and automated execution, and will become the core carrier for brands to serve users in the future. Leading technology companies are all ramping up efforts to build enterprise-grade AI infrastructure, and the AI-specific search layer has emerged as a hot new track. Brands can advance related technology deployment early to optimize their own AI service capabilities.

Clear on-ground growth opportunities have emerged in the AI agent space, with key directional insights for industry practitioners as follows:

1. Changes in market and demand: Competition in the AI sector has shifted from the original race over model parameters, benchmark scores, and launch hype to validation based on real-world usage volume. Only tracks with real, high-frequency user demand are the future growth directions. Currently, self-evolving general agents and AI-specific intelligent search layers are fast-growing incremental areas.

2. Opportunity and risk reminders: Security compliance has become a core competitiveness for AI products. One of the core reasons Hermes Agent outperformed OpenClaw is that it addressed developers' pain points by solving security issues. Security features such as zero-telemetry design and automatic sensitive information desensitization are far more popular among users. The traditional static skill maintenance model can no longer meet market demand, and autonomous learning and evolution is the core user pain point that practitioners can center their layout around.

The rapid development of AI agents brings multiple inspirations for factories advancing digital transformation and exploring new business opportunities, with key takeaways as follows:

1. New business opportunities: The AI agent industry has entered a period of rapid expansion, with new segmented tracks emerging continuously. Beyond general-purpose agents, enterprise-grade Agent operation infrastructure and AI-specific search infrastructure are both new incremental markets. Factories with relevant technical capabilities can enter these supporting fields to tap new growth space.

2. Insights for digital transformation: When advancing the deployment of digital AI applications, factories should not only pursue superficial metrics such as parameter size and benchmark ranking. Instead, they need to focus on solving real user pain points, and prioritize improving AI's autonomous learning capability and data security compliance to achieve sustained real usage and tangible deployment value.

3. Guidance for product development direction: Traditional AI products with static functions no longer match market demand, while AI products supporting self-evolution and self-optimization better align with user needs. This direction can serve as a reference for factories developing their own digital tools or exporting AI-related products to external clients.

Current development trends of the AI agent industry are clear, revealing definite customer pain points and bringing new business opportunities for AI service providers, with key insights as follows:

1. Overall industry development trend: AI agents have exited the proof-of-concept phase and entered the stage of real-world deployment competition. The industry has formed two clear development mainstreams: connective agents capable of self-evolution and continuous optimization, and the integration of AI search engines and the agent search layer. These two paths will eventually converge in depth, with the ultimate goal of building task-solving agents that combine strong execution and judgment capabilities.

2. Core customer pain points and solution directions: The developer community has long faced the pain point that AI cannot learn autonomously from experience, while market demand for data security and privacy compliance is growing steadily. Traditional products with static skill maintenance and security loopholes can no longer meet demand. Service providers can prioritize development of core capabilities such as self-evolving persistent memory, autonomous learning and optimization, and privacy-safe desensitization, and can also enter the new track of retrieval infrastructure purpose-built for AI agents.

The development of AI agents clarifies the development direction for AI platforms and outlines risks to avoid, with key takeaways as follows:

1. Changes in core user demand for AI platforms: User demand has shifted from superficial conversational capability to complex task execution capability, requiring AI to have core capabilities including autonomous evolutionary learning, security compliance, and accurate real-time search. Platforms need to adjust their product development strategies around these demands.

2. Reference for latest industry layout: Global leading technology companies are now all prioritizing layout of enterprise-grade Agent operation infrastructure to solve engineering challenges such as fragmented context, permission management, and reliable execution. The retrieval layer purpose-built for AI Agents has become a new core infrastructure track, and platforms can layout in this area based on their own advantages.

3. Risk avoidance reminders: Platforms need to attach great importance to security issues. OpenClaw once suffered a serious security crisis that hurt its growth. Platforms can adopt security designs such as zero-telemetry and automatic information desensitization, while prioritizing real user experience rather than only chasing launch hype and benchmark ranking.

This article discloses the latest industry changes in the AI agent field and sorts out new characteristics of industry development, offering high reference value for relevant research, with core content as follows:

1. Latest industry developments: Hermes Agent topping the OpenRouter usage ranking in May 2026 marks that AI agents have officially entered the era of multi-path competition, with noticeable changes in the global AI industry landscape. Chinese models now hold most seats in the core support tier, and the focus of industry competition has shifted from model parameters and benchmark scores to validation based on real-world usage volume.

2. Current industry development landscape: AI agents are currently developing along two clear main paths: one is the connective Agent path capable of self-evolution and continuous optimization, and the other is the path integrating AI search engines and the agent search layer. The two paths are not mutually exclusive and are converging, with the ultimate goal of realizing Agentic AI that transforms AI from a conversational tool into a task solver that combines execution capability and judgment.

3. New research-worthy questions: Traditional static AI architectures cannot meet users' demand for autonomous learning, security compliance is a core industry pain point, and enterprise applications still need to solve engineering challenges such as fragmented context and permission management. All these are directions worthy of in-depth research.