---
title: "Industry Roundtable | LLMs and AI Agents Will Define the Next Shape of ToB Apps"
author: "Unique Research"
sourcePublication: "Unique Research (FFCAP English library)"
originalPublishedAt: "2024-03-20T16:30:00+08:00"
canonical: "https://ffcap.cn/en/research/20240320a06ne200"
source: "https://ffcap.cn/en/research/20240320a06ne200"
language: "en"
cnSourceUrl: "https://view.inews.qq.com/a/20240320A06NE200"
---

# Industry Roundtable | LLMs and AI Agents Will Define the Next Shape of ToB Apps

_Unique Research · Industry insight / roundtable · March 20, 2024_

## Background

On **January 24, 2024**, Unique Capital and partners held the **2024 AIGC Application Development Summit** in Shanghai (~1,000 AI practitioners). Edited from the guest roundtable. **Note:** Lanma Technology (澜码) founder Zhou Jian appears as a panel guest — not a partner welcome retag.

## Guests

-   **Zhou Jian** — Founder & CEO, Lanma Technology (澜码科技)
-   **Yang Jinsong** — Founder & CEO, Future-Form Intelligence / AutoAgents.ai (未来式智能)
-   **Liu Yuchen** — Founder, Tyrion AI
-   **Wang Shouyan** — Founder, Deep Insight (DeepBI / DBI)

## Q: What are you shipping in LLMs / agents?

**Wang (Deep Insight):** Product **DBI** — AI-driven data insight, not classic BI. Traditional BI assumes humans analyze and prioritizes visualization; DBI assumes AI should autonomously analyze raw data and surface insights (“data-led insight”).

**Liu (Tyrion):** AI-agent development platform + end-to-end agent solutions — expert knowledge bases, expert agents, and process automation for agent-doable tasks.

**Yang (Future-Form):** Multi-agent solutions for global enterprise scenarios — orchestration for knowledge-era AI automation pipelines.

**Zhou (Lanma):** Started before “agent” was trendy — LLM automation for business processes. From RPA days: workers handle data, documents, apps, processes → RAG for docs, text-to-API for data. Agents must be process-aware (know which stage) to prioritize work — key difference vs copilots.

## Q: Brand scenarios — data, users, traffic; how do agents reshape them? (Liu’s brand-side background)

**Liu:** Early ChatGPT experiments analyzing BA ↔ private-domain consumer chat logs beat classic NLP on semantics/attitude/entity mention. Expert knowledge once lived in salespeople’s notebooks — digitize for training and offline→online handoff. Optimistic on foundation models + middle-layer agents/RAG as B-side infrastructure improves.

## Q: Lanma AskXBOT’s three steps → ToB landing?

**Zhou:** (1) **Digitize expert knowledge** — knowledge leaves with experts (consultancies as lead magnets). Agents can ingest Excel dumps from unintegrated procurement/finance systems and write API multi-dim tables — CEO gives an account, not a system rewrite. (2) **CUI flexible interaction** — leaders won’t learn 300-field wide tables; build semantic + metric layers so machines adapt to humans. (3) **Domain knowledge loop** — JD/resume screening example: humans correct machine scores; rules for “job stability,” training-mill resumes, etc. become digital; experts scale from 100→1000 cases with AI assist.

## Q: AutoAgents.ai “async collaborative” agent orgs?

**Yang:** Serial multi-agent patterns (AutoGPT / Stanford Town) amplify hallucination in enterprise use. Real work is parallel. Design: central scheduler agent decomposes tasks; sub-agents work in parallel with mutual cross-checks → usable outputs. Short-term: hallucination control; long-term: compresses human knowledge collaboration — LLM as brain, agents as roles/teams.

## Q: Beyond query — DeepBI’s deeper value?

**Wang:** Visualization is for humans; models are token-sensitive to raw data. Goal: **thinking** → deep insight via collaboration + agent organization. Products: assisted analysis (any size table, ask anything) and auto analysis (machine explores without looping) — “infinite thinking” BI via agents.

## Q: Third-party LLMs as black boxes — control & vendor relations?

**Wang:** Models ≈ elite universities graduating en masse — still hallucinate; orgs need training, constraints, workflows. Value models for domain knowledge + logic; occasional errors can spark exploration.

**Liu:** Models = gasoline; apps = cars; middle layer = engine/transmission — workflow, RAG, prompt, sometimes fine-tune + multi-model routing. Like cloud providers vs industry software.

**Yang:** Models = bricks; scarce skill is scenario landing. App side feeds model vendors (longer context, stable JSON, eval which model wins which task).

**Zhou:** Three value tiers: (1) integrate formerly unusable docs/chats via RAG — open 6B/13B often enough; (2) data analysis — medium difficulty; (3) master agent that actively asks / GPT-4-class function calling — likely 100B–trillion-param, costly. Apps are the “menu”; don’t use premium meat for mapo tofu. Moat: private SOP + private data enterprises won’t put on public SaaS — “palace kung pao with ordinary ingredients.”

## Q: AI-native vendors — new customer relationships?

**Wang:** Classic Chinese ToB: ¥100Ms revenue needing 1,000+ people because rigid codebases. Math-trained colleagues now adapt across industries — tens of engineers for 10 verticals vs 100–200 before → ToB cost collapse.

**Liu:** Agents let customers edit behavior in visual UIs without vendor redeploys; future NL goals + history learning → business users configure agents. Outcome-oriented SaaS.

**Yang:** Buy results not tools (vs 2-week SAP training). Pricing by completed replies/tasks. Delivery shifts from algo experts to IT/business users — text-to-agent shortens long enterprise delivery cycles.

**Zhou:** Outcome-share pricing vs SaaS seat pricing. Example: ~¥1 cost for high-quality resume recommendations lifting interview adoption **2–3×**; Softcom Power (~80k staff / ~2k recruiters at ¥6–8k salaries) would pay ¥5–10 per better shortlist — digital-employee economics from RPA days becoming real.

## Q: Advice for AI founders in the new year?

**Wang:** ToB can become decreasing marginal cost. Entering existing markets (e.g., $10Bs BI) needs ~**10×** better product — AI now makes that possible at ~1/10 cost.

**Liu:** 2023 was literacy; by year-end brands planned real AI projects (phase 2–3). Optimistic on 2024.

**Yang:** Human–AI hybrid workflows (e.g., 70–80 of 100 nodes by AI); agents as new entry alongside web/app. Incremental scenario value.

**Zhou:** Agent count doubling ~every 12 months; inference cost /10 every 18 months; training /4. Zhipu GLM-4 ~3–6 months ahead of his GPT-4 catch-up expectation; domestic models to catch GPT-4 this year. CUI consensus exists but 2023 lacked scenarios due to hallucination/cost/learning — opportunity for agent cos. Zhipu valuation ¥2B→¥20B creates revenue pressure; 150 of China Top 500 already partners — need real business landing = agent vendors’ upside.

_4 CN images; attach on ingest. Lanma kept as interview guest (inventory type interview), not partner._

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Editorial English edition published on UniqueCapital: https://ffcap.cn/en/research/20240320a06ne200
On-site reading page: https://ffcap.cn/en/research/20240320a06ne200