According to Ethan Mollick on X, citing Jonathan Berant, current AI models struggle with agent‑to‑agent coordination because they exhibit low discourse coherence even at high information density, which flattens or degrades performance across many tasks (as reported by Ethan Mollick referencing Jonathan Berant’s analysis on X). According to Jonathan Berant on X, classic NLP measures reveal that when models communicate using twice as many tokens as humans, their coherence deficits still hinder task success, underscoring that today’s LLMs are not optimized for multi‑agent hand‑offs. As reported by Ethan Mollick, this creates near‑term business opportunities for tooling that structures inter‑agent messages, enforces concise state summaries, and validates task‑handoffs, especially in autonomous operations, customer support triage, and workflow orchestration. According to Jonathan Berant, advancing multi‑agent systems will require standardized coordination protocols, discourse‑aware planning, and evaluation benchmarks tailored to information hand‑off quality rather than token volume.