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Policy control for AI agents means enforcing explicit rules when an agent attempts to act. Brane provides policy control by evaluating Python policy functions before and after capability execution. Policies receive a structured PolicyContext and return a structured Decision.

Policy Control Loop

This loop lets teams decide what an agent can do based on:
  • Agent identity
  • Principal identity
  • Tenant
  • Environment
  • Capability risk
  • Input arguments
  • Output
  • Runtime metadata
  • Application-specific policy data

Before Policies

Before policies run before the action executes. Use them to prevent unsafe side effects.

After Policies

After policies run after execution and can inspect output. Use them to detect oversized outputs, unexpected schemas, PII, secrets, or policy violations after the capability returns.

Policy Control Categories