Safety checks add latency to every agent action. The policy engine evaluates rules. The content scanner inspects text. The audit system writes receipts. In well-configured systems, this overhead is under 10 milliseconds. In poorly configured systems, it can exceed 500 milliseconds. This guide covers the most effective optimizations.
Instrument your safety pipeline to measure the time spent in each component:
Focus optimization efforts on the component consuming the most time.
Reduce rule count. Each rule is evaluated sequentially until a match is found. Fewer rules mean faster evaluation. Consolidate rules that share the same action but differ only in tool name.
Order rules by frequency. Place rules that match the most common tool calls at the top. The engine stops evaluating after the first match, so frequently matched rules should be checked first.
Simplify regex patterns. Complex regex with backtracking can be slow on long inputs. Use anchored patterns (^SELECT instead of .*SELECT) and avoid nested quantifiers.
Scan selectively. Not every action needs content scanning. Read-only operations on internal databases produce predictable outputs that do not need PII scanning. Configure scanning rules to skip known-safe tool calls.
Tune detection rules. Disable detection rules that are not relevant to your use case. A code assistant does not need the medical PII detector.
Set input size limits. Scanning very large text inputs is expensive. Set maximum input sizes for scanning and handle oversized inputs with a separate policy rule.
Batch writes. If your deployment processes many actions per second, batch audit receipt writes to PostgreSQL instead of writing each one individually. Use a small buffer (50 to 100ms) to group writes.
Async when acceptable. If your compliance requirements allow it, write audit receipts asynchronously. The action proceeds immediately while the receipt is written in the background. This removes audit writing from the critical path but introduces a small window where a receipt might not yet be persisted.
Cache policy evaluation results. If the same tool call with the same parameters is evaluated repeatedly, cache the result. Invalidate the cache when the policy changes.
Monitor performance continuously. Latency can degrade as policies grow, traffic patterns change, and new detection rules are added. Regular performance reviews catch regressions before they affect user experience.
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