The Boundaries of Power: AI Multi-agent Organizational Permission Design through the Lens of Economic Classics
This article explores how to map Coase's transaction cost theory and Hayek's dispersed knowledge proposition onto AI systems. The core argument posits that permission design should shift from static control to dynamic intent drift management, achieving a balance between control granularity and response agility by establishing feedback loops.
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With the rise of one-person companies and multi-agent collaboration models, traditional centralized permission management is facing failure. The background lies in the fact that AI agents, when executing complex tasks, often involve knowledge flow and decision authorization across organizational boundaries. The problem is that overly tightening permissions increases coordination entropy, while excessive delegation leads to actual outcomes deviating from expected outcomes. This article argues for drawing on Coase's arguments regarding firm boundaries and Hayek's respect for tacit knowledge to construct a dynamic permission system based on world model calibration. This system should focus not only on the granting of permission but also on ensuring the falsifiability of AI behavior through cross-validation, thereby maintaining the organization's judgment in uncertain environments. Through this institutional functional analysis method, we can transform legal compliance into executable engineering constraints.
Hayek pointed out that effective coordination must respect the contextuality of knowledge. In a multi-agent environment, the reliability of AI cannot be guaranteed solely by centralized instructions but must be anchored through traceable context. This means permission design must support the attachment of evidence chains, binding calibration reports with authorization acts. When an AI agent performs tasks in a specific environment, its permission should be based on a real-time assessment of the current world model. By establishing feedback loops, the system can continuously monitor deviations, ensuring that the AI's judgment aligns with actual needs. This design acknowledges the ineffability of knowledge, transforming permission from a rigid entry permit into a dynamic measure of trust, thereby achieving efficient collaboration within dispersed knowledge structures.
Coase's theory reminds us that organizational boundaries depend on the trade-off between internal coordination costs and market transaction costs. In an AI permission architecture, this means establishing a layered intervention mechanism to optimize governance costs. The underlying infrastructure should provide deterministic interruption permission to handle severe intent drift; the application layer should support negotiated degradation, reducing coordination entropy through games between multi-agents. When external supervision costs rise, internal auditing capabilities must be enhanced synchronously. By linking permission changes to the integrity of the causal chain, a one-person company can maintain agility while ensuring that every authorization decision has falsifiable logical support, thus maintaining governance stability within dynamic boundaries.
Ultimately, the permission design of AI organizations should not be a simple configuration of technical parameters, but a profound experiment in institutional function. The transferable insight is that the essence of permission is the right to regulate information flow. By introducing cross-validation and dynamic calibration, we can internalize externalities as the evolutionary driving force of the system. Future organizations will no longer rely on rigid hierarchies but will achieve the efficient unification of knowledge discovery and task execution through sophisticated feedback loops in the continuous alignment of expected outcomes and actual outcomes. This paradigm shift from control to coordination will be the key to building high-survivability AI systems.
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