Guardrails for AI Agents

Guardrails are protective mechanisms that ensure AI agents behave safely and predictably by preventing harmful outputs and unauthorized actions. They operate at three levels: prompt-level (instructional boundaries in system prompts), model-level (built-in safety filters for toxicity and PII), and action-level (workflow permissions and tool access controls). Common guardrail types include relevance classifiers for scope management, safety classifiers for detecting prompt injections, rules-based protections against known threats, moderation filters for inappropriate content, and tool safeguards that assess risk levels of third-party integrations. Frameworks like NeMo Guardrails, Guardrails AI, and LangChain provide tools for implementing these protective layers in AI workflows.