---
title: "AI Agents Will Be Standard Across Industry Applications — Open Day Roundtable"
author: "Unique Research"
sourcePublication: "Unique Research (FFCAP English library)"
originalPublishedAt: "2023-09-27T18:30:00+08:00"
canonical: "https://ffcap.cn/en/research/20230927a089z000"
source: "https://ffcap.cn/en/research/20230927a089z000"
language: "en"
cnSourceUrl: "https://view.inews.qq.com/a/20230927A089Z000"
---

# AI Agents Will Be Standard Across Industry Applications — Open Day Roundtable

_Unique Research · Insight / AIGC Online Open Day · September 27, 2023 · Full editorial rewrite of ~3,896 CJK — four company intros + full Q&A preserved_

Unique Open Day on **AI Agents** — advantages, enterprise landing, and coexistence with humans. Guests: Lanma Tech, Waveform Intelligence, CAMEL-AI.org, AutoAgents.ai.

## Companies

### Lanma Technology (澜码科技)

Founded 2023; vision: “everyone can design their own AI Agents.” LLM-based next-gen automation platform — a “data flywheel” that learns skills from PC/phone/meeting scenes to build proprietary multimodal foundation models.

### Waveform Intelligence (波形智能)

AI interactive-content platform from tools into full entertainment-chain coverage. Own creation tools: rich content, interactive, personalized, multimodal — beyond wrapping GPT APIs. Differentiator: **interactive long-text generation** with infinite interactive content; vertical interactive models via full-stack NLP LLM capability. Paper: _RecurrentGPT_ on the initial framework.

### CAMEL-AI.org

Open-source community for autonomous & communicative agents — study behavior, capabilities, risks at scale; ships agents, tasks, prompts, models, datasets, sim environments. Goal: understand human–AI coexistence — cooperation/competition forms, new economic/legal/education systems, accelerate science/engineering.

### AutoAgents.ai (未来式智能)

China AI/cloud firm on the front line of enterprise digitalization. Team from Alibaba DAMO, Tencent, ByteDance, AWS, Google — LLMs, multimodal, self-evolving agents, intelligent decision. Product: agents that plan/decompose from user goals, connect data/apps. Scenes: intelligent CS, customer-centric sales, employee-centric recruiting, business analytics.

## Roundtable

### Trends next few years

**Zhou Jian @ Lanma:** Breakthrough moment — LLMs regenerate software, especially language understanding; “super individuals” will wield LLM skills for many tasks.

**Jiang Yuchen @ Waveform:** Agent power tracks base LM power. Email-class tasks: base LMs already suffice; agents mainly provoke the model. Literary creation: base LMs fall short — agents matter more. Agents become standard in every app; choose train-own / train-with-framework / API by scene.

**Li Guohao @ CAMEL:** Near term, high-accuracy scenes still need human-in-the-loop (often passive collaboration) — science, software. Entertainment/creative (novels, game AI, animation) easier (imagination > absolute accuracy). Academic priority: controllable generation under format constraints.

**Yang Jinsong @ AutoAgents:** Uneven landing as models/tools iterate — education/CS first; high-stakes later. Long term: multiple intelligent services around each user.

### Advantages, problems, impact

**Lanma:** Hardest: expert _tacit_ knowledge (e.g. spotting résumé fraud needs context). Agents that capture tacit/operational/embodied knowledge differentiate from public LLMs; enterprises keep that knowledge private — large commercial opportunity.

**Waveform:** Is an “agent” only a GPT API, or layers beyond LLM (e.g. ontology)? Content creation needs both model and agent-framework innovation plus business-model innovation. Entrances change; basic human needs don’t — tech must serve hard needs.

**CAMEL:** Role split: humans higher-level; agents call APIs for concrete decisions — risk of unsafe/biased outcomes (e.g. hiring). Alignment is hard to define; **safety** is clearer — do not harm humans.

**AutoAgents:** Agents disrupt SaaS/ToB UX — intent understanding, creative scene-adaptive interaction. ToB software as middle layer to data tables may shrink as users talk to tables via agents. Generated software cost becomes measurable → usage-based pricing vs feature bundles. Deep domain knowledge remains a moat; more companies sell APIs than monolithic SaaS.

### Enterprise landing blockers

**Lanma:** Compute, data management, integration, privacy compliance, informatization maturity.

**Waveform:** Non-standardized enterprise data; fuzzy requirements/acceptance — users expect ~100% accuracy models rarely deliver.

**CAMEL:** Broader society questions — coexistence, cooperation/competition, ethics/law as agents evolve.

**AutoAgents:** Weak reasoning/planning and hallucination — narrow to scenes; constrain LLMs; three-layer best practice (industry / enterprise / individual) to raise completion probability.

### Coexistence

**Lanma:** Agents may approach GAIA-like sensing/control of society — ethics, self-awareness, motives; steer for long-term human interest.

**Waveform:** More social bonds with virtual agents in entertainment; productivity + spiritual richness, with interpersonal-relationship concerns.

**AutoAgents:** Short-term productivity vs resource imbalance and self-iterating agents that drift from human interest — ethics, regulation, global collaboration.

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Editorial English edition published on UniqueCapital: https://ffcap.cn/en/research/20230927a089z000
On-site reading page: https://ffcap.cn/en/research/20230927a089z000