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One thing I’m still unclear on: what runtime signal is the soft-rule evaluator actually binding to when it decides “semantic drift”?

In other words, what is the enforcement unit the policy is attached to in practice... a step, a plan node, a tool invocation, or the agent instance as a whole?

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Tool invocation. Each time the agent emits a tool call, the evaluator assesses it against the original task intent plus a rolling window of recent tool results.

We tried coarser units (plan nodes, full steps) but drift compounds fast, by the time a step finishes, the agent may have already chained 3-4 bad calls. Tool-level gives the tightest correction loop. The cost is ~200ms latency per invocation. For hot paths we sample (every 3rd call, or only on tool-category changes) rather than evaluate exhaustively.

That makes sense binding to the smallest viable control surface, and the sampling strategy for hot paths sounds like a pragmatic balance between latency and coverage. Thanks for the additional feedback here.

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