Guardrails
Guardrails check what goes into and comes out of your AI agents, and act on what they find. They run on work.studio's servers, so they apply whatever app or device the conversation comes from.
Where a guardrail checks
What a guardrail can do
What a guardrail can check
- Personal information: emails, phone numbers, card numbers, SSNs, IBANs, passport numbers, addresses and more
- Secrets: API keys, tokens, private keys and database credentials
- Prompt injection: attempts to override an agent's instructions
- Content: blocked words, restricted topics and patterns. Moderation categories such as hate, harassment and violence, judged by a model
- Format: length, language, JSON that matches a schema, required keys, links that resolve
- Policy: which tools and models may be used, and request metadata
- Your own logic: a webhook, a workflow, or an AWS Bedrock guardrail
Attach or enforce
Attach a guardrail to an agent on the agent's Safety tab. It then applies to that agent's conversations.
Enforce a guardrail for an environment to apply it to every AI call there. This covers every agent, workflow AI step and generator. If it also checks retrieved knowledge, documents are checked as they are indexed into a knowledge base. Users can't turn an enforced guardrail off. Enforcing needs the
guardrail:managepermission.
Try a guardrail before you rely on it
- Monitor mode records what each rule would have done, and changes nothing.
- The Test tab runs the guardrail on sample text and shows each rule's verdict and timing.
- Test cases are run when the guardrail is approved. Approval is refused if one fails.
- Guardrail Activity, under Governance, lists every finding: what the rule asked for, what happened, and whether it took effect. The matched text itself is never recorded.