RFC-020: Governed Autonomy, Symbolic Intent, and D-DNA Evidence — 1. Abstract
AIGP Specification › RFC-020: Governed Autonomy, Symbolic Intent, and D-DNA Evidence › 1. Abstract
Section index · 2. Motivation →
RFC-020: Governed Autonomy, Symbolic Intent, and D-DNA Evidence
PRIVATE AND PROPRIETARY — NOT A PUBLIC RFC. Owned by Kanjani AI Research & Causum. See NOTICE.md.
Status: Draft Version: 0.1 Protocol Family: AIGP Applies To: Agentic AI, autonomous AI, embodied AI, robotic systems, drones, autonomous aircraft components, cyber-physical systems, hybrid human-machine actors, and force-capable autonomous systems Author: AIGP Protocol Contributors
1. Abstract
This RFC defines the foundational AIGP model for governing agentic and autonomous AI systems.
The first generation of AI governance focused primarily on prompts, model invocations, responses, data use, and audit records. That model is necessary but insufficient for systems that act. Agentic and autonomous AI systems do not merely generate information; they plan, invoke tools, move through environments, modify systems, interact with people, affect infrastructure, and may alter the physical or psychological state of human beings.
This RFC introduces the concept of Governed Autonomy: autonomous action bounded by human-authorized intent, machine-verifiable constraints, runtime enforcement state, redundant distributed enforcement, and D-DNA signed temporal evidence.
The central principle is:
Human language may express intent, but autonomous systems must be governed by machine-verifiable constraints and proven through replayable D-DNA evidence.