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

AI agent guardrails prevent harmful, unauthorized, or costly actions. Input guardrails: content filtering (block harmful or off-topic requests), prompt injection detection (identify attempts to manipulate agent behavior), and input validation (verify data formats and ranges). Action guardrails: tool allowlists (agents can only use pre-approved tools), parameter validation (bound numerical inputs, sanitize string inputs), confirmation requirements (require human approval for destructive or irreversible actions), and spending limits (cap API costs, prevent excessive resource consumption). Output guardrails: response filtering (block PII leakage, confidential information, harmful content), factuality checking (verify claims against known sources), and format validation (ensure structured outputs conform to expected schemas). Implementation: use NeMo Guardrails (NVIDIA's framework for LLM guardrails), Guardrails AI (Python framework for validating LLM outputs), or custom middleware that intercepts agent inputs and outputs. Behavioral guardrails: set maximum iteration limits (prevent infinite loops), implement timeout per agent step, track cumulative cost and halt when budget exceeded, and implement rollback capability for multi-step actions. Monitoring and alerting: log all agent decisions and actions, alert on unusual patterns (excessive tool calls, repeated failures, unexpected tool usage), and implement kill switches for immediate agent shutdown. Testing: red-team agents with adversarial inputs, test boundary conditions for all guardrails, verify that guardrails don't block legitimate use cases, and run automated guardrail regression tests. Progressive autonomy: start with human-in-the-loop for all actions, gradually increase autonomy as trust is established, and maintain human override capability at all times.