--- title: "Interview | AutoAgents.ai’s Yang Jinsong: Intent-Native Agent Software Will Open a New Era of Enterprise Services" author: "Unique Research" sourcePublication: "Unique Research (FFCAP English library)" originalPublishedAt: "2023-12-26T16:30:00+08:00" canonical: "https://ffcap.cn/en/research/20231226a06ao000" source: "https://ffcap.cn/en/research/20231226a06ao000" language: "en" cnSourceUrl: "https://view.inews.qq.com/a/20231226A06AO000" --- # Interview | AutoAgents.ai’s Yang Jinsong: Intent-Native Agent Software Will Open a New Era of Enterprise Services _Unique Research · Interview · December 26, 2023 · Author (CN): Tan Qiuping · Guest: Yang Jinsong, Founder & CEO, AutoAgents.ai_ In April, Stanford and Google Research published _Generative Agents: Interactive Simulacra of Human Behavior_ — the Smallville “virtual town” of 25 agents living, chatting, and socializing with distinct personalities, backstories, and interests. First author **Joon Sung Park** stressed that LLMs/ChatGPT matter, but the decisive piece is a generative-agent architecture with memory, perception/action, and planning/reflection modules — computational agents that behave like credible humans with long-horizon consistency. Many practitioners called it an early AGI marker. That wave pulled veterans into new companies. **Yang Jinsong** — former DAMO Academy product/commercialization lead and GM of DAMO green-energy AI who shipped the big-data+AI platform “Alibaba Lingjie” — left Big Tech after watching ChatGPT’s leap in natural-language intent understanding (late 2022) and co-founded **AutoAgents.ai** (未来式智能) for enterprise Agent landing: natural-language + visual-drag platforms so business users can build custom Agents and chase **10×** work efficiency. Yang graduated from Peking University Yuanpei (CS). Besides DAMO he led Feishu AI & search product at ByteDance and worked at AWS. His products twice won WAIC SAIL awards, twice made DAMO’s Top-10 tech trends, and entered World Intelligence Congress / XinhuaNet digital-model cases. Co-founders span Alibaba, Tencent, and Google — a rare China LLM-application team with both product R&D depth and commercialization scars. Unique Research spoke with Yang on Agent practice and foresight. ## Intent-native Agent software is a large opportunity From Turing’s 1936 abstract machine and Colossus programming to Tukey’s 1958 “software,” software has been the main human–machine medium for ~40 years. Developers flatten every feature for discoverability — PPT alone allegedly has **2,000+** features — while software never knew what humans truly wanted. The gap is chronic over-/under-use. “With generative AI and Agents,” Yang told Unique Research, “a new software form can respond to human intent, raise efficiency and outcomes, break old constraints, and open a great new opportunity.” Every enterprise will normalize AI tools — and, deeper still, re-iterate bottom business processes with AI-native thinking. Agents are among the best paths for scaled LLM landing. That thesis, plus the team’s prior AI capital, birthed AutoAgents.ai. ## Three Agent application classes **(a) Fix human–machine asymmetry.** Work that burns human hours on collection and summarization — low cognitive value — is exactly what Agents do well. **(b) Agents as a new traffic entry.** Instead of hunting a buried app feature, users speak to an Agent that carries business logic and feels natural. **(c) Multi-agent autonomous collaboration with humans.** Highest form: role- and goal-assigned Agents explore an environment together — Smallville is the archetype. Multi-agent dialogue/collaboration is a core research topic because single Agents are capacity-capped, just as humans need orgs. AutoAgents.ai therefore designs Agent org structure, teams, and inter-Agent links that mirror human collaboration. The company aims to be an **“Agent factory”** Multi-Agent framework so enterprises can mint their own Agents for cost and efficiency. ## Six technical distinctives 1) No-code Agent products for business users; 2) natural-language SOP → auto-generated Agent apps; 3) patented industry-first **asynchronous collaborative** Agent orgs; 4) first autonomous-agent product proven in live business scenes; 5) **Agent-Tuning**; 6) growing Agent code-generation ability. Competitiveness hinges on Agent intelligence and complex-task completion. AutoAgents.ai keeps the “brain” (base LLM) sharp via Agent Tuning and tool-use training; Memory via RAG plus best-practice/SOP knowledge with reward labeling to reinforce good judgments. Raising code-gen and tool packaging from natural language is treated as SOTA work — a paper aimed at next year’s **ACL**. Async collaborative orgs break serial-only patterns (1:N, N:N, broadcast). Users may only talk to an entry Agent that dispatches task Agents. Enterprises do not start from a blank board: each Agent ships with APIs/tools, embedded best practices and knowledge, plus custom policy overrides. ## Two core products **Lingda (灵搭)** — LLM app builder for business users: NL goal planning, workflow/tool generation, visual drag-and-drop; 1–2 people can ship an LLM app. **Overseas social Autonomous Agents** — coach + operator for China go-global / local SMB social accounts: diagnose pain points, auto-build strategy, cycle execution with multi-Agent loops that simulate human social ops. LLM vendors welcome the stack because models gain real work beyond chat. AutoAgents.ai partners with open-API model providers so enterprise users can call Agent capabilities when needed. Typical architecture: infra (compute/data) → models → mid-layer frameworks like Lingda → apps/verticals. The firm will first standardize energy, finance, government, and marketing solutions, then broaden. Yang’s guardrails: help clients prepare for human–AI collaboration (a transformation as large as digitalization); avoid thin features a model vendor can absorb in a few iterations by staying close to vertical customer pain regardless of base-model churn. ## Closing — remake the enterprise value chain Citing Marc Andreessen at a16z Connect/Enterprise: generative AI opens new product space and exposes incumbent fragility — a double opportunity for startups. A year ago AutoAgents.ai itself was unimaginable. “Agents are the hands and feet of LLM thought,” Yang said; with reasoning and planning they open a new human–machine era and remake the full enterprise value chain. When Agents go mainstream and best practice is highly SOPified, domain know-how may even be purchasable. The company’s founding aim remains an enterprise LLM + scene platform that puts AI power in every knowledge worker’s hands. _3 CN images; attach on ingest. Author (CN): Tan Qiuping; edit: Qiuping._ — END — --- Editorial English edition published on UniqueCapital: https://ffcap.cn/en/research/20231226a06ao000 On-site reading page: https://ffcap.cn/en/research/20231226a06ao000