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This research addresses the risks of uncontrolled remembering in AI workflows.

April 30, 2026Open Access

Descartes-10 Continuity Capsule Protocol: A Public Overview of Governed State Continuity for AI-Assisted Workflows

Key Points

This research addresses the risks of uncontrolled remembering in AI workflows.
Introduces the Descartes-10 Continuity Capsule Protocol (D10-CCP) for managing AI memory and context.
Proposes an architecture separating active state, stable context, and discarded memory.
Highlights the need for controlled continuity in AI-assisted workflows.
Suggests that existing memory mechanisms can propagate errors or outdated information.

Abstract

The central risk in long-running AI-assisted workflows is not merely forgetting, but uncontrolled remembering. Existing systems often treat memory, transcript replay, and compressed summaries as benign continuity mechanisms, yet these mechanisms can silently carry forward obsolete assumptions, unstable context, or distorted intent. The Descartes-10 Continuity Capsule Protocol (D10-CCP) proposes a governed continuity-capsule architecture that separates stable context, active state, discarded history, operator profile, and continuity invariants, allowing prior context to support resumption without silently mutating present action. This document publicly discloses the protocol’s architecture, central principle, and intended scope in order to establish authorship and priority, while expressly withholding the canonical schema, validator logic, conformance suite, domain-pack specification, implementation evaluation notes, and broader Descartes-10 governance spine.

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