AI Guardrails
Safety mechanisms that constrain AI system behaviour — ranging from content filters and output restrictions to structural governance enforcement.
AI guardrails are mechanisms that constrain what AI systems can do. They exist on a spectrum of enforcement strength:
- Weak guardrails: System prompts, guidelines, content policies (behavioural — the AI is instructed but can deviate) - Medium guardrails: Output filters, content moderation, human review queues (detective — problems caught after generation) - Strong guardrails: Structural enforcement via governance gates, tool-level constraints, infrastructure-level controls (preventive — violations impossible)
Most current AI governance relies on weak and medium guardrails. This is adequate for conversational AI (chatbots) but inadequate for agentic AI (agents that take real-world actions).
The term "guardrails" can be misleading if it implies that governance is just about preventing bad outputs. Governance is about authority, accountability, and institutional structure — guardrails are one mechanism within a broader governance framework.
How Constellation handles this
Constellation implements the strongest form of guardrails: structural enforcement via governance gates. Tool calls are intercepted before execution, making constraint violations structurally impossible rather than behaviourally discouraged.