Skip to content

Guardrails Module

Overview

The guardrails module in the AgenticAI Framework enforces safety, compliance, and quality constraints on AI-generated outputs. It ensures that responses adhere to predefined rules, ethical guidelines, and domain-specific requirements before being delivered to the end user.

Key Classes and Functions

  • Guardrail — Base class for defining custom guardrails.
  • ContentFilter — Filters out disallowed or unsafe content.
  • PolicyEnforcer — Applies organizational or legal policies to AI outputs.
  • validate_output(output) — Validates generated content against all active guardrails.
  • add_guardrail(guardrail) — Dynamically adds a new guardrail at runtime.

Example Usage

from agenticaiframework.guardrails import ContentFilter, PolicyEnforcer

# Initialize guardrails
filter_guardrail = ContentFilter(blocked_keywords=["confidential", "classified"])
policy_guardrail = PolicyEnforcer(policies=["no_personal_data"])

# Validate output
output = "This is a confidential document."
if not filter_guardrail.validate_output(output):
    print("Output blocked due to sensitive content.")

Use Cases

  • Preventing the disclosure of sensitive or confidential information.
  • Enforcing compliance with legal and regulatory requirements.
  • Maintaining brand voice and tone consistency.
  • Filtering out harmful, biased, or toxic content.

Best Practices

  • Combine multiple guardrails for layered protection.
  • Regularly update blocked keywords and policies.
  • Test guardrails with diverse datasets to ensure robustness.
  • Log blocked outputs for auditing and improvement.