Your AI Agent Returns 200 and Is Wrong: The Silent-Success Drift Pattern

Dev.to / 6/2/2026

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Key Points

Many AI agent monitoring tools treat runs as “successful” when model calls and tool responses return 200/ok, but they don’t verify whether the real-world outcome matched the intended effect.
The article describes “silent-success drift,” where execution succeeds while the outcome drifts from intent, causing issues like incorrect invoices that may appear only days later.
To detect this, it recommends logging three underused signal classes, starting with a pre-action “intent line” before every tool call that records expected outcome and rollback artifacts.
The key point is that adding lightweight (about 10 minutes) structured wiring changes to capture intent and outcome-related signals is more important than relying solely on vendor LLM-call envelope instrumentation.
The author argues that catching outcome drift requires instrumentation that connects what the agent intended to do with verifiable indicators of the effect on the world, not just API-level success.

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