AutoAgents.ai (未来式智能) has closed a Pre-A funding round from Fanchuang Capital, Zhongguancun Capital,...
The AMW Read
Incremental update: AutoAgents is a known Chinese agent-platform startup; Pre-A funding and product launch confirm trajectory but do not resolve an open debate or introduce a new top-tier entrant.
AutoAgents.ai (未来式智能) has closed a Pre-A funding round from Fanchuang Capital, Zhongguancun Capital, and Tanyuan Capital, with existing backers Dongzheng Innovation and Linge Venture Capital following on. The company will use the capital for compute investment, team expansion, and ecosystem development for its new product line. Founded in June 2023 by a team from Alibaba DAMO Academy, Tencent, ByteDance, and Google, AutoAgents focuses on enterprise agent platforms. Its flagship product, LingDa (灵搭), is an enterprise-grade low-code agent builder serving power, finance, and manufacturing sectors. In 2025, revenue grew 4× from a single-digit-million RMB base, targeting RMB 100M (~$14M) in 2026. The company also launched DaiDai (袋袋), an agent-as-a-service marketplace where users can hire domain-specific digital experts on a pay-per-outcome basis. DaiDai has reached over RMB 10M (~$1.4M) in ARR from early customers.
Why it matters: AutoAgents sits at the intersection of the enterprise agent-building platform and the marketplace for composable digital labor — a pattern we track as the 'agent labor marketplace' emerging within the AI Agents segment. The company claims 100% renewal over 20 power-grid customers, signaling strong product-market fit in high-compliance verticals. The 'Harness Engineering' feedback loop between LingDa (build) and DaiDai (consume) mirrors the flywheel that platform players like UiPath and ServiceNow aim to create, but applied to LLM-native agents. This deal also exemplifies the shift from 'train your own agent' to 'hire a pre-trained specialist' that CEO Yang Jinsong articulates, which could accelerate enterprise adoption by lowering the skill barrier.
Expert take: AutoAgents is executing a disciplined vertical-first strategy in power and finance, which are sticky, high-budget sectors with deep compliance needs that create a moat against horizontal agent platforms. The DaiDai marketplace introduces an interesting variable-cost model for enterprise AI consumption: risk-free in small doses (pay per outcome), scalable if the agent works. The challenge will be maintaining task quality at scale across hundreds of expert domains, and avoiding the quality dilution that plagued earlier outsourced AI services. The Pre-A round size is undisclosed but likely below $20M given the stage, so capital efficiency matters.