大二 CTO、60人团队和一家 Agent Native 公司

This article shares the entrepreneurial experience and replicable organizational methodology of Yuhe Technology, a native AI Agent startup, with key takeaways as follows:

1. Core division of labor: The team splits work into three tiers. Humans are only responsible for proposing ideas, making judgments and setting directions at the first tier, while information organization, execution and other work at the second and third tiers are delegated to AI Agents, with humans only conducting acceptance checks and spot inspections. For product development, the team first builds the Loop Engineer framework, defines acceptance criteria and test cases, then lets Agents develop functions in parallel, with daily token consumption reaching thousands of RMB. This methodology is fully replicable.

2. Hiring and management experience: Hiring focuses on three core qualities: passion for AI, native Agent thinking, and an entrepreneurial mindset. For product and R&D roles, the team prioritizes young, talented candidates; for marketing roles, it prefers experienced candidates with open minds. Management adopts an open and transparent approach that allows employees to challenge decisions, shares information broadly, and focuses on results to motivate team members. This management approach is also a valuable reference for general entrepreneurs.

This article introduces entrepreneurial practices in the native Agent space, offering multiple references for brands pursuing AI transformation and operational efficiency improvement, with key takeaways as follows:

1. Reference for business efficiency improvement: Yuhe Technology's main revenue driver today is its pre-sales digital employee product, which helps brands replace manual work such as pre-sales proposal drafting, cuts labor costs, improves pre-sales response speed, and solves the problem of insufficient pre-sales team capacity.

2. Reference for product R&D: By having Agents take over non-core work at the second and third tiers, brands can free up R&D manpower to focus on core judgment, reduce R&D iteration costs, and speed up new product launches — an approach well-suited for brands exploring AI-related product implementation.

3. Reference for collaboration and industry trends: Yuhe currently uses its FDE (Field Development Engineering) capability to complete the last mile of product implementation, adapting to the customized needs of brands across different scenarios. Delivery costs will gradually decrease as products mature. In the long run, Agent technology will restructure brand job roles, requiring early planning and adjustment to adapt to changes.

This article outlines the current status of entrepreneurship in the Agent track, providing directional guidance and experience for sellers looking to enter the AI space, with key takeaways as follows:

1. Market opportunity insights: The native Agent track is still in an early stage of development with enormous market potential. Existing market demand far outstrips the service capacity of current industry players. In particular, enterprise demand for AI transformation is booming; many enterprises that attempt to build Agents in-house fail to meet expectations and will eventually turn to third-party services, creating abundant opportunities in the track.

2. Replicable experience: Sellers can adopt this human + Agent division of labor methodology, letting AI take over execution-focused work to reduce labor costs and improve overall operational efficiency. For hiring, sellers can reference Yuhe's criteria: hire young, talented open-minded new hires for product and R&D innovation, and retain experienced employees receptive to new ideas for business development.

3. Risk warning: The biggest bottleneck in the industry today is a significant shortage of qualified AI talent. Sellers planning to enter the track should reserve talent in advance to avoid capacity shortages that cannot keep up with market demand.

The native Agent collaboration methodology shared in this article provides reference for factories advancing digital transformation and exploring new business opportunities, with key takeaways as follows:

1. Business opportunities: On one hand, factories can leverage Agent technology to transform their production, design and business processes to cut costs and improve efficiency. On the other hand, factories can develop vertical Agent products for the manufacturing sector and enter the enterprise AI service track. This track currently has unmet demand, as many enterprises struggle to pull off successful transformation, leaving large room for market exploration.

2. Insights for digital transformation: Factories can adopt Yuhe's three-tier human + Agent division of labor model: delegate information collection, organization and repetitive execution work to Agents, while factory technicians and managers only focus on core direction setting, innovation and result acceptance. This frees up substantial manpower to focus on core business and reduces operational costs.

3. Insights for organizational adjustment: Agents will restructure the positioning of traditional job roles. Factories need to adjust internal job design, cultivate new talent with native Agent thinking, and adapt to new working models to advance digital implementation more effectively.

This article introduces the latest implementation practices of native Agent transformation services, providing references for industry trends and solutions for B2B AI service providers, with key takeaways as follows:

1. Industry development trends: Enterprise demand for AI transformation is currently booming. The native Agent model is reshaping organizational division of labor and job design across enterprises. The track offers enormous overall market size, remains in an early development stage, and has a large volume of unmet customer demand, creating abundant room for business expansion.

2. Core customer pain points: Many enterprises seeking transformation have attempted to build Agents in-house, but actual implementation results fall far short of expectations. Meanwhile, mature Agent products are not yet ready for out-of-the-box use, creating a clear gap between off-the-shelf products and actual customer needs that service providers must address to complete the last mile of implementation.

3. Reference solutions: Service providers can learn from Yuhe's model by positioning FDE as a capability rather than a standalone role: FDE teams work with business development to respond to customer needs and generate customized proposals before contracting, then fill the product-demand gap during post-contract delivery. As products mature, providers can gradually reduce the share of FDE work and eventually transition to a standardized product business model, while adopting the internal human + Agent division of labor to improve efficiency and control costs.

The operational experience of this native Agent startup shared in this article provides reference for ecosystem layout and internal operations for AI-focused platform operators, with key takeaways as follows:

1. Market demand and layout direction: The biggest bottleneck in the native Agent space today is a talent shortage, with insufficient supply of qualified talent with native Agent thinking. Platforms can launch targeted talent matching and training services to meet the core needs of startups. Meanwhile, Agent track startups have high growth potential, so platforms can prioritize recruiting these companies to enrich their ecosystem.

2. Reference for internal operation and management: Yuhe's open, transparent, results-oriented management model is well worth learning. The approach avoids top-down pressure, encourages divergent opinions, lets the best idea win, grants full information and authority to core members to stimulate initiative, and uses results as the core evaluation metric — all of which effectively improve organizational efficiency and serve as a strong reference for innovative platforms.

3. Ecosystem service direction: Platforms can organize and share this replicable Agent collaboration methodology with their incubated startups, increasing the value platforms provide to merchants and boosting platform stickiness.

This article provides a vivid practical case of a domestic native Agent startup, offering high reference value for AI industry research, with core information as follows:

1. New industry trends: The native Agent model has moved from concept to implementation. A 60-person native Agent startup has already emerged in China, giving rise to a set of entirely new job roles such as Agent product manager, Agent designer, and Harness Engineer. The positioning of traditional product, development and design roles is being restructured, and the human + Agent division of labor has emerged as a new organizational form.

2. New industry challenges: The biggest limiting factor for the Agent industry today is not technology, but talent shortage: there is insufficient supply of compound talent with passion for AI, native Agent thinking and an entrepreneurial mindset. At the same time, Agent products are not yet fully mature, and there is a widespread gap between product capabilities and customer demand that requires delivery-side teams to fill.

3. Researchable business model: Yuhe currently adopts a "standardized product + customized FDE delivery" model, and will gradually evolve into a pure standardized product company as its products mature. This division of labor methodology can be replicated across multiple fields, providing a clear, actionable entrepreneurial path for the industry.