# 《对智能体模型的批判》

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-06-26 09:19
- AIHOT 分数：53
- AIHOT 链接：https://aihot.virxact.com/items/cmqu8z13e02cgsl80apx8yxwq
- 原文链接：https://x.com/rohanpaul_ai/status/2070315948528251047

## AI 摘要

该论文质疑当前将所有能力强AI系统称为“agent”的做法，指出许多所谓的agent只是围绕LLM的高级工作流，而非独立智能体。复杂行为不等于自我导向行为。论文提出核心区分：“agentic AI”（看似自主）与“agentive AI”（能动性源于系统内部），并构建Goal-Identity-Configurator模型，要求AI保持长期目标、更新自我认知、预测结果并自主决定思考深度，从真实和模拟经验中学习。论文主要构建论点和架构，未测试完整系统。

## 正文

This paper pushes back on the habit of calling every capable AI system an "agent" and asks the cleaner question： what makes something an agent in the 1st place？

Explains why today's AI agents are mostly clever tools， not truly independent agents.

The problem is that many systems called agents are really advanced workflows around LLMs， not independent actors.

Complex behavior is not the same as self-directed behavior.

A chess engine can crush a grandmaster without wanting anything， and a browser agent can complete a task without maintaining a durable sense of what it is， what it can do， or why this task matters beyond the current instruction.

They can call tools， follow steps， and complete useful tasks， but their goals， roles， limits， and update cycles still mostly come from humans.

The paper's core idea is to separate "agentic AI" from "agentive AI"， where agentic means it looks autonomous and agentive means its agency comes from inside the system.

The authors propose the Goal-Identity-Configurator model， where an AI keeps long-term goals， updates its sense of itself， predicts possible outcomes， decides how much to think， and learns from real and simulated experience.

They do not mainly test a finished system， but build an argument and architecture for what real machine agency would require.

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Link - arxiv. org/abs/2606.23991

Title： "Critique of Agent Model"