Guardrails
Guardrails constrain AI behavior through validation, policies, permissions, fallbacks, and human review.
Use guardrails when model output or tool use can affect user trust, money, permissions, safety, or data exposure.
Use when
- Tool-using agents
- Regulated or sensitive domains
- User-generated prompts
- Production AI workflows
Avoid when
- Replacing evaluation
- Fixing unclear product requirements
- Hiding unsafe tool permissions
- Making impossible guarantees
What guardrails should do
Guardrails reduce risk around model behavior. They can validate structured output, block unsafe actions, enforce permissions, limit tool scope, route to human review, or provide fallbacks.
They work best when paired with evaluation. A guardrail that has never been tested is mostly a hopeful rule.
Where guardrails fail
Guardrails cannot make a vague product safe. If the user goal, allowed actions, or failure handling is unclear, guardrails become scattered patches.
Common mistakes
- Treating moderation as the only guardrail.
- Giving agents broad tools and adding rules afterward.
- Blocking too much without a recovery path.
- Skipping human review for high-impact actions.
Next decision
For tool-using systems, define allowed actions, validation rules, and escalation paths before expanding autonomy.