Guardrails and runtime validation
Validate inputs and outputs at runtime so a malformed, unsafe, or off-policy response never reaches a user or a downstream system.
Check both ends
Guardrails sit around the agent and enforce policy: filtering unsafe or off-topic inputs before they reach the model, and validating outputs before they leave. They are the last line of defense when the model itself gets something wrong.
Gate risky actions
For anything irreversible or high-stakes, require an explicit check before it executes: a validation rule, a confirmation, or a policy match. Frameworks like Guardrails AI and NeMo Guardrails provide reusable building blocks for this.
Watch for over-blocking
A guardrail that blocks everything is as useless as one that blocks nothing. Measure both how often it correctly stops bad output and how often it wrongly stops good output, and tune for both.
Key takeaways
- Validate inputs and outputs at runtime as a last line of defense.
- Gate irreversible or high-stakes actions behind an explicit check.
- Tune guardrails to avoid over-blocking legitimate behavior.
Further reading
- Guardrails AI guardrailsai.comAn open framework for validating agent inputs and outputs against policies at runtime.
- NeMo Guardrails NVIDIANVIDIA's toolkit for adding programmable safety and topic guardrails around a conversational agent.