# Why AI Agent Teams Get Stuck > Source: > Published: 2026-08-02 16:06:04+00:00 Member-only story # Why AI Agent Teams Get Stuck ## Multi-Agent LLMs, MACE, and the Hidden Collaboration Problem *A new AI research paper shows that agentic AI systems can look collaborative while quietly failing at one of the most basic team skills: learning who to ask.* Research explainer for AI enthusiasts and builders | Based on Choi et al. (2026) [1] AI agents are supposed to collaborate, debate, delegate, and learn from each other. That promise sits behind the current wave of agentic AI, multi-agent systems, autonomous AI workflows, and enterprise AI orchestration. But the research paper “Multi-Agent LLMs Fail to Explore Each Other” makes a sharp and uncomfortable point: today’s LLM agents often do not explore their teammates properly. They can pick one peer too early, stick with that choice, and miss better collaborators [1]. That matters because multi-agent AI systems are being designed for serious work: research, coding, analysis, planning, operations, customer support, decision support, and enterprise automation. If agents cannot figure out which other agent knows what, the whole system can look active and collaborative while behaving like a narrow loop. The paper introduces a lightweight fix called MACE, short for Multi-Agent Contextual Exploration. MACE gives agents a structured way to test different collaborators before settling into a pattern [1].