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Working through policy engine design decisions and want outside perspective.
SidClaw evaluates policies by explicit priority ordering. If two policies match the same action, the higher-priority policy wins. Simple. Predictable. But it means you have to manually assign priorities, and conflicts are resolved by a number you set months ago and forgot about.
Alternative: specificity-based evaluation, like CSS. A policy matching agent:support-bot AND action:send-email AND recipient:*.internal beats a policy matching action:send-email because it's more specific. No manual priority needed. But the mental model is harder to debug when things go wrong.
We went with explicit priority for v0.1 because:
Compliance teams want deterministic, auditable evaluation order
FINRA examiners will ask "why was this action allowed?" and "because it had the highest priority number" is a clearer answer than "because CSS specificity rules"
You can always inspect the policy list sorted by priority and trace exactly which one fired
But specificity has real advantages for complex deployments with hundreds of policies.
Anyone running a policy engine at scale? What's worked, what's been painful?
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Working through policy engine design decisions and want outside perspective.
SidClaw evaluates policies by explicit priority ordering. If two policies match the same action, the higher-priority policy wins. Simple. Predictable. But it means you have to manually assign priorities, and conflicts are resolved by a number you set months ago and forgot about.
Alternative: specificity-based evaluation, like CSS. A policy matching
agent:support-bot AND action:send-email AND recipient:*.internalbeats a policy matchingaction:send-emailbecause it's more specific. No manual priority needed. But the mental model is harder to debug when things go wrong.We went with explicit priority for v0.1 because:
But specificity has real advantages for complex deployments with hundreds of policies.
Anyone running a policy engine at scale? What's worked, what's been painful?
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