AI Agent Safety and Guardrails

Autonomous AI agents can take actions with real-world consequences, making safety
guardrails a critical design concern. Effective guardrails operate at multiple
layers: input validation, output filtering, action approval, and behavioral
monitoring.

Input validation screens user prompts for injection attacks and out-of-scope
requests before they reach the LLM. Output filtering checks model responses for
harmful content, PII leakage, and hallucinated facts before they are returned to
the user or passed to downstream tools.

Action approval gates prevent agents from executing high-risk operations without
human confirmation. A tool allowlist restricts which external APIs an agent can
call, while rate limits cap the number of actions per session.

Behavioral monitoring detects drift from expected patterns. If an agent suddenly
makes far more tool calls than usual, repeats the same action in a loop, or its
error rate spikes, an alert should fire. Statistical methods like Z-score
analysis on span metrics can identify anomalous sessions in real time.

Defense in depth combines all these layers so that no single failure leads to
unsafe behavior. Logging every decision in an observable trace makes post-incident
analysis possible and builds trust in autonomous agent systems.
