What is the difference between AI guardrails and AI governance?

AI guardrails filter what an AI model SAYS (output validation), while AI governance controls what an AI agent DOES (execution enforcement) — guardrails are necessary for content safety but fundamentally insufficient for agent security because they operate on the wrong layer.

The critical distinction:

• Guardrails (Guardrails AI, NeMo): Validate model outputs after generation — "Is this response toxic? Does it contain PII? Is it on-topic?" Operates on text. Uses LLM-based classification (probabilistic)
• Governance (Exogram): Validates agent actions before execution — "Should this database query execute? Should this API call proceed? Should this file be modified?" Operates on actions. Uses deterministic code (0% error rate)

Why guardrails fail for agent security:

• A guardrail can verify the model's response looks safe, but it can't verify the tool call triggered by that response is safe
• Guardrails use probabilistic classification — they have false negatives. One missed classification = one destructive action in production
• Guardrails add 200-800ms of LLM inference latency. Exogram evaluates in 0.07ms with zero LLM calls

Use guardrails for content safety. Use Exogram for execution safety. They're complementary, not competitive — but only governance prevents production incidents.