IntentBound
A governance-oriented control framework for constraining autonomous artificial intelligence through explicit intent, enforceable boundaries, and accountable execution.
Overview
IntentBound is an ethical and governance framework designed to ensure that autonomous AI systems operate strictly within their authorized purpose. By binding system behavior to declared intent and predefined limits, IntentBound reduces the risk of unintended actions, scope expansion, and misaligned outcomes.
Why Intent Matters
As AI systems gain autonomy, traditional permission models and static safeguards become insufficient. Autonomous agents may continue acting beyond task completion, optimize unintended objectives, or access domains that were never explicitly authorized.
IntentBound addresses this challenge by treating intent as a first-class governance primitive — something that must be declared, enforced, and auditable throughout execution.
Core Principles
- Declared Intent: A clear, machine-readable specification of the task objective and purpose.
- Operational Boundaries: Explicit limits on tools, resources, environments, and domains.
- Termination Conditions: Defined criteria for stopping, pausing, or escalating execution.
- Verification & Oversight: Continuous or gated validation of actions against authorized intent.
Ethical Relevance
IntentBound contributes to ethical AI by increasing transparency, reducing unintended harm, and supporting accountability. By explicitly linking actions to authorized intent, it enables clearer responsibility chains and more effective human oversight of autonomous systems.
Governance & Policy Context
Frameworks like IntentBound are increasingly relevant in AI governance, regulatory compliance, and institutional oversight — particularly in high-impact or safety-critical deployments. They complement alignment efforts by focusing on authorization, legitimacy, and operational control.