How do you implement guardrails and safety controls for AI agents?

Agent guardrails prevent harmful or unintended actions. Input guardrails validate user requests, blocking prompt injection attempts, off-topic requests, and requests for unauthorized actions. Action guardrails control what the agent can do by implementing allowlists of permitted tool calls, requiring confirmation for destructive operations, enforcing parameter range restrictions, and limiting the scope of data the agent can access or modify. Output guardrails check generated responses for PII leakage, harmful content, and factual accuracy. Budget guardrails limit the number of LLM calls, tool executions, and total cost per interaction. Operational guardrails include maximum execution time limits, maximum number of reasoning steps, and deadman switches that terminate runaway processes. Implement a comprehensive audit log of all agent actions for compliance and debugging. Use sandbox environments for testing new agent capabilities before production deployment. NVIDIA NeMo Guardrails and Guardrails AI provide frameworks for implementing these safety controls.