Enterprise governance requires decisions, evidence and accountable authority, yet not every review task must remain a manual process. We introduce a task‑substitution framework for Digital Governance Frameworks (DGF) that treats each gate as an executable contract. Substitution is allowed only when sufficient information is accessible, decision and authority checks are valid, and the total human work after accounting for exceptions, verification, correction and maintenance is reduced. From this we derive a residual‑work threshold and explain why automating most cases can still increase labor. Forward‑deployed engineering maps these conditions to an architecture of agents, rule engines, evidence services and escalation paths. DGF‑Bench supplies controlled evidence from 300 synthetic projects and 899 evaluable model‑project runs. Gemini 3.8 Flash, GPT‑5.6 Luna and DeepSeek v4.1 Flash achieve strict‑gate success rates of 94.98 %, 83.29 % and 74.18 %; complete‑route success rates are 76.92 %, 42.33 % and 24.67 %. A deterministic control passes all 1,700 gates given the supplied rules and structured facts, showing that the comparison is reproducible when executing the provided decision kernel. Evidence audits and 135 repeated runs distinguish correct decisions from reliable execution. A document counterexample demonstrates an information‑sufficiency obstruction. These results support the technical feasibility of replacing specified governance‑review tasks with agents and software. The framework also defines a workforce test based on total human effort at fixed output and quality; the present measurements focus on review performance. All source code, dossiers, traces and analyses are publicly available.
Review