Why Agents Need Economics, Not Just Prompts
Most agent frameworks focus on orchestration — who calls whom, in what order, with what context window. This is important engineering work. But it misses the deeper question: why would an agent cooperate?
The Orchestration Trap
When we design multi-agent systems today, we implicitly assume that all agents share a common objective function. Agent A calls Agent B, passes context, gets a result. The system works because we wrote all the agents and hardcoded their cooperation.
But this breaks the moment agents have different principals — different users, different organizations, different economic interests. In the real world, cooperation is not a given. It must be incentivized.
What DeFi Taught Us
Decentralized Finance solved a version of this problem for capital. AMMs, bonding curves, and mechanism design created systems where self-interested actors produce collectively beneficial outcomes — not through trust, but through economic architecture.
The same principles apply to agents:
- Staking: Agents put skin in the game before participating
- Bonding curves: Agent services priced by supply and demand
- Slashing: Bad behavior has real economic consequences
Toward Agent Economics
This is the thesis behind what I'm building: the next leap in multi-agent systems isn't better prompting or more sophisticated RAG pipelines. It's economic infrastructure — on-chain primitives that align agent incentives at the protocol level.
The question isn't "how do I make GPT-4 and Claude work together?" It's "how do I create a market where any agent can offer services, stake reputation, and get paid — trustlessly?"
This is an early articulation of an idea I expect to develop further. Timestamped here for future reference.
大多数智能体框架关注的是编排——谁调用谁、以什么顺序、用什么上下文窗口。这是重要的工程工作。但它忽略了一个更深层的问题：智能体为什么要合作？
编排陷阱
当我们今天设计多智能体系统时，我们隐含地假设所有智能体共享一个共同的目标函数。智能体 A 调用智能体 B，传递上下文，获得结果。系统之所以能工作，是因为我们编写了所有智能体并硬编码了它们的合作关系。
但当智能体有不同的委托人——不同的用户、不同的组织、不同的经济利益时，这就行不通了。在现实世界中，合作不是理所当然的。它必须被激励。
DeFi 教会了我们什么
去中心化金融为资本解决了这个问题的一个版本。AMM、绑定曲线和机制设计创造了这样的系统：自利的参与者产生集体有益的结果——不是通过信任，而是通过经济架构。
同样的原则适用于智能体：
- 质押：智能体在参与之前投入利益
- 绑定曲线：智能体服务按供需定价
- 惩罚机制：不良行为承担真实的经济后果
迈向智能体经济学
这就是我正在构建的核心论点：多智能体系统的下一次飞跃不是更好的提示词或更复杂的 RAG 管线。而是经济基础设施——在协议层面对齐智能体激励的链上原语。
问题不是"如何让 GPT-4 和 Claude 一起工作？"而是"如何创建一个市场，让任何智能体都能提供服务、质押声誉、并获得支付——无需信任？"
这是一个我预期会进一步发展的想法的早期表述。在此标注时间以供未来参考。