Continuous Evaluation at Scale

Evaluating policies continuously can degrade performance if poorly implemented.

A naive engine:

while True:
    for policy in all_policies:
        policy.evaluate(system_state)

while True:
    for policy in all_policies:
        policy.evaluate(system_state)

while True:
    for policy in all_policies:
        policy.evaluate(system_state)

This approach does not scale.

Selective Evaluation Strategy

Track which state variables each policy depends on:

policy_dependencies = {
    "auto_scale": ["cpu", "request_rate"],
    "failover": ["dependency_health"]
}

policy_dependencies = {
    "auto_scale": ["cpu", "request_rate"],
    "failover": ["dependency_health"]
}

policy_dependencies = {
    "auto_scale": ["cpu", "request_rate"],
    "failover": ["dependency_health"]
}

When state updates:

def on_update(changed_keys):
    for policy, deps in policy_dependencies.items():
        if any(key in deps for key in changed_keys):
            evaluate(policy)

def on_update(changed_keys):
    for policy, deps in policy_dependencies.items():
        if any(key in deps for key in changed_keys):
            evaluate(policy)

def on_update(changed_keys):
    for policy, deps in policy_dependencies.items():
        if any(key in deps for key in changed_keys):
            evaluate(policy)

Only relevant policies execute.

Deterministic Execution

Policy engines must guarantee:

• Idempotent execution

• Ordered evaluation

• Conflict resolution

Without determinism, automation creates instability.

Scale requires architectural discipline.