RFC-021: AIGP-SGL — Symbolic Governance Language for Agentic and Autonomous AI — 1. Abstract
AIGP Specification › RFC-021: AIGP-SGL — Symbolic Governance Language for Agentic and Autonomous AI › 1. Abstract
Section index · 2. Motivation →
RFC-021: AIGP-SGL — Symbolic Governance Language for Agentic and Autonomous AI
PRIVATE AND PROPRIETARY — NOT A PUBLIC RFC. Owned by Kanjani AI Research & Causum. See NOTICE.md.
Status: Draft Version: 0.1 Protocol Family: AIGP Depends On: RFC-020 Governed Autonomy, Symbolic Intent, and D-DNA Evidence Related RFCs: RFC-022 AIGP-VGL, RFC-023 ENFORCE Broadcast, RFC-024 D-DNA for Governed Autonomy, RFC-025 Cognitive Harm Governance Applies To: Agentic AI, autonomous AI, embodied AI, drones, robotics, autonomous aircraft components, cyber-physical systems, hybrid human-machine actors, and force-capable systems
1. Abstract
AIGP-SGL defines a symbolic, typed, machine-verifiable governance language for agentic and autonomous AI systems.
AIGP-SGL exists because human language is insufficient as a runtime governance surface. Human language can express intent, doctrine, law, ethics, mission objectives, and post-action explanation, but autonomous systems require constraints that can be parsed, evaluated, enforced, signed, and replayed.
AIGP-SGL provides a compact formal notation for expressing governance intent, authority, permission, prohibition, obligation, boundary, escalation, inhibition, evidence, and consequence.
AIGP-SGL is not a natural language. AIGP-SGL is not an ethical reasoning engine. AIGP-SGL is not the runtime enforcement engine itself.
AIGP-SGL is a symbolic governance notation that compiles into a canonical abstract syntax tree and machine-enforceable constraints.
The central principle is:
Human language may express intent, but autonomous systems must be governed by symbolic, typed, machine-verifiable constraints.