Workflow Signal Protocol: A Measurement Method for Deployment-Time Workflow Observability in Legal AI

Abstract

Legal AI benchmarks, citation checks, and retrieval-grounding tests primarily evaluate upstream capability: whether a model can answer, extract, or ground a legal task. Deployment asks a different question: whether a particular output remains observable enough to be deployed, reviewed, corrected, or escalated once it enters an organizational workflow. We introduce the Workflow Signal Protocol (WSP), a deployment-layer measurement method for recording workflow observability as a structured workflow-observability record. WSP encodes source status, proposition support, review state, recourse, provenance, and role-scoped disclosure. We validate WSP through controlled stress tests, public legal datasets, documented real-world failures, and a live-output pilot using three general-purpose model application programming interface (API) arms. In the main matched-vocabulary stress test, local formal/substantive routing reduced hidden-risk deployment from 93.5% under calibration-only abstention to 4.0% or below; all 192 pilot outputs were expressible as WSP records. These results support the central claim that deployment-time workflow observability is measurable within the evaluated legal-AI settings; validation in deployed institutional legal workflows remains future work.