AI Agents · Lesson

Policy Enforcement for Agent Actions

Pre-action policy checks, allowlists/denylists, and dynamic policy rules.

What Is Agent Policy Enforcement?

Policy enforcement is the runtime gate that runs before every agent action to decide whether the action is permitted. Without it, the agent's only constraint is the LLM's instruction-following — which can be bypassed or misinterpreted.

Enforcement must be outside the LLM, in your infrastructure.

The Pre-Action Check Pattern

Before executing any tool, call can_agent_do(action, context). This function is the single enforcement point — every path to action execution passes through it.

def can_agent_do(action: str, context: dict) -> tuple[bool, str]:
    '''
    Returns (allowed: bool, reason: str).
    Context includes: user_id, agent_id, session_id, parameters, timestamp.
    '''
    # 1. Check denylist first (fast path for obvious violations)
    if action in DENIED_ACTIONS:
        return False, f'Action "{action}" is on the global denylist'

    # 2. Check allowlist
    if action not in ALLOWED_ACTIONS:
        return False, f'Action "{action}" is not on the allowlist'

    # 3. Context-specific checks
    return check_context_policy(action, context)

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