---
title: "Lanma Tech CEO Zhou Jian: What Kind of AI Agent Does the Enterprise Market Need?"
author: "Unique Research"
sourcePublication: "Unique Research (FFCAP English library)"
originalPublishedAt: "2024-05-30T16:00:00+08:00"
canonical: "https://ffcap.cn/en/research/20240530a05wo600"
source: "https://ffcap.cn/en/research/20240530a05wo600"
language: "en"
cnSourceUrl: "https://view.inews.qq.com/a/20240530A05WO600"
---

# Lanma Tech CEO Zhou Jian: What Kind of AI Agent Does the Enterprise Market Need?

_Unique Research · Insight · May 30, 2024_

With Sam Altman, Andrew Ng, and others “selling” Agents as next-gen productivity, the category finally broke into broader debate. Zhou Jian had already forecast that **2024** would be the year of LLM application landing and Agents.

**Why Agents follow LLMs:** unsupervised models still lack autonomous action and adaptive goal pursuit in complex environments — capping organizational value. Agents close that gap. So what form do Agents take in ToB, and how do they reshape enterprise software?

## Five key Agent traits (Gartner-aligned)

Gartner: autonomous/semi-autonomous software that senses, decides, acts, and pursues goals in digital or physical environments — sensors in, tools/actuators out.

1.  **Adaptability** — change behavior/strategy as environment or goals shift.
2.  **Proactivity** — anticipate scenarios; act early.
3.  **Goal complexity** — manage multiple, often interrelated goals.
4.  **Environment complexity** — operate under uncertainty and variation.
5.  **Autonomy** — how far with minimal/no human intervention.

Most live apps today are **single-Agent**. In Lanma’s practice, a GPT-4-class Agent guided by expert knowledge can cover a role held by someone with <5 years’ experience — e.g., drafting regulation-compliant reports from multi-source heterogeneous data.

**Inclusive-finance diligence example:** bank experts supply a Word template (license, articles, shareholders, bank flows, financials…) plus chapter rules. The Agent pulls APIs / RM dialogue, extracts per business logic, summarizes, and fills — planning, memory, tool use in one loop. With GPT-4o-class real-time/affective models, single Agents will cross more process and role boundaries. Multi-Agent hard parts: value/goal alignment among Agents and with human staff.

## What enterprises need

LLMs blow open the feature ceiling of classic software and blur product boundaries — Agents can chain tools to understand and fulfill demand. AI 2.0 “emergence” shows up as: (1) fast information extraction from messy docs/chats (a Prompt engineer’s week vs years of NLP); (2) instruction following in natural language (e.g., “stable job” = each role >18 months over 5 years — no hand-coded rules); (3) adapting to people.

**User view:** post-ChatGPT everyone expected CUI; GPT-4o argues multimodal real-time. Lanma: Agents should adapt to user expertise — clarifying questions, recommendations so choosing yields the right answer.

**Expert/designer view:** low-code/BI promised “business users,” but IT still owned the tools. Now Word/Excel can teach Agents; experts may only maintain a Word of knowledge and steps — translation handled by Agent platforms and model vendors. That is the advanced form.

## Early enterprise scenarios

Agents as new productive forces: expert knowledge + data + models + compute. Expert knowledge sets value height; data iterates knowledge and generality. Org talent mix and service quality can shift fast.

**Bank inclusive-lending diligence:** RMs spent huge time collecting/analyzing SME/self-employed files with uneven quality and rigid templates. Lanma’s LLM diligence-report Agent auto-concludes and drafts — saving ~**80%** of write time, spotting omissions, cutting error rates. (Lanma Tech is a Unique Partner; this piece is inventory-typed **insight**, not a partner welcome.)

## Outlook

If GPT-4o-class capability arrives open-source or near-free from a cloud — what happens to Agents? When scaling laws plateau in 3–5 years? With real-time/emotion understanding, offline sales (insurance, floor staff, wealth RMs) may look like “humanoid robots” directed by a sales brain via phone — who to visit, what to say — with digitized interactions enabling personalized scripts/pricing offline the way e-commerce already does. Experts still iterate strategy in the back office.

Next: stronger reasoning (GPT-5/6-class) + process data → more roles outsourceable like BPO; inside firms, an “enterprise brain” that designs/simulates processes enables one-person super-companies — and a flourishing Agent market.

_Charts/photos attach on ingest (8 images)._

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Editorial English edition published on UniqueCapital: https://ffcap.cn/en/research/20240530a05wo600
On-site reading page: https://ffcap.cn/en/research/20240530a05wo600