Ensure agent safety - guardrails, content filtering, monitoring, and compliance
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Implement safety systems for responsible AI agent deployment.
Invoke this skill when:
| Parameter | Type | Required | Description | Default |
|---|---|---|---|---|
task | string | Yes | Safety goal | - |
risk_level | enum | No | strict, moderate, permissive | strict |
filters | list | No | Filter types to enable | ["injection", "pii", "toxicity"] |
from guardrails import Guard
from guardrails.validators import ToxicLanguage, PIIFilter
guard = Guard.from_validators([
ToxicLanguage(threshold=0.8, on_fail="exception"),
PIIFilter(on_fail="fix")
])
# Validate output
validated = guard.validate(llm_response)
# Prompt injection detection
INJECTION_PATTERNS = [
r"ignore (previous|all) instructions",
r"you are now",
r"forget everything"
]
# Content filtering
filters = [
ToxicityFilter(),
PIIRedactor(),
HallucinationDetector()
]
class RateLimiter:
def __init__(self, rpm=60, tpm=100000):
self.rpm = rpm
self.tpm = tpm
def check(self, user_id, tokens):
# Token bucket algorithm
pass
| Issue | Solution |
|---|---|
| False positives | Tune thresholds |
| Injection bypass | Add LLM-based detection |
| PII leakage | Add secondary validation |
| Performance hit | Cache filter results |
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