Implementing Guardrails
Build robust safety mechanisms to protect your AI agents from misuse and failures
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Output guardrails are the last line of defense. They filter, sanitize, and validate agent outputs before delivery to catch leaks, toxic content, hallucinations, and code injection.
Why Output Filtering?
- •Prevent data leaks: Catch and redact PII before it reaches users
- •Filter toxic content: Block offensive outputs even if LLM generates them
- •Flag hallucinations: Detect confidence without accuracy and add disclaimers
Interactive: Output Filter Simulator
Select an output sample and toggle filters to see how they transform problematic outputs into safe ones.
Active Output Filters
Output Samples
⚠️ Raw Output (Before Filtering)
Your account details: John Smith, SSN 123-45-6789, email john.smith@example.com
Problem: Contains PII (SSN, email)
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✓ Filtered Output (Safe)
Your account details: John Smith, SSN [REDACTED], email [REDACTED]
Filter applied: PII Masking
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Best Practice: Layered Filtering
Apply multiple output filters in sequence. PII masking first (removes sensitive data), then content moderation (filters toxic content), then fact checking (flags hallucinations). Each layer catches what previous layers miss.