Context Lake: A System Class Defined by Decision Coherence
Xiaowei Jiang

TL;DR
This paper introduces the concept of Context Lake, a new system class designed to ensure decision coherence among AI agents operating over shared resources, addressing limitations of existing data systems.
Contribution
It formalizes the Decision Coherence Law, proves the Composition Impossibility Theorem, and defines the necessary architectural features of Context Lakes for correct multi-agent decision-making.
Findings
No existing system class satisfies Decision Coherence requirements.
The Composition Impossibility Theorem shows independent systems cannot achieve Decision Coherence together.
Context Lakes must support semantic operations, transactional consistency, and bounded staleness.
Abstract
AI agents are increasingly the primary consumers of data, operating continuously to make concurrent, irreversible decisions. Traditional data systems designed for human analysis cycles become correctness bottlenecks under this operating regime. When multiple agents operate over shared resources, their actions interact before reconciliation is possible. Correctness guarantees that apply after the decision window therefore fail to prevent conflicts. We introduce the Decision Coherence Law: for agents that take irreversible actions whose effects interact, correctness requires that interacting decisions be evaluated against a coherent representation of reality at the moment they are made. We show that no existing system class satisfies this requirement and prove through the Composition Impossibility Theorem that independently advancing systems cannot be composed to provide Decision…
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Taxonomy
TopicsMulti-Agent Systems and Negotiation · Ethics and Social Impacts of AI · Explainable Artificial Intelligence (XAI)
