System Design

Opening — Why this matters now Multi-agent LLM systems have quietly become the industry’s favorite way to brute-force intelligence. When one model struggles, the instinct is simple: add more agents. Vote harder. Debate longer. Spend more tokens. And yet, performance curves keep telling the same unflattering story: early gains, fast saturation, wasted compute. This paper asks the uncomfortable question most agent frameworks politely ignore: why does scaling stall so quickly—and what actually moves the needle once it does? The answer, it turns out, has less to do with how many agents you run, and more to do with how different they truly are. ...

The recent wave of innovation in AI systems has ushered in two distinct design paradigms—AI Agents and Agentic AI. While these may sound like mere terminological variations, the conceptual taxonomy separating them is foundational. As explored in Sapkota et al.’s comprehensive review, failing to recognize these distinctions risks not only poor architectural decisions but also suboptimal performance, misaligned safety protocols, and bloated systems. This article breaks down why this taxonomy matters, the implications of its misapplication, and how we apply these lessons to design Cognaptus’ own multi-agent framework: XAgent. ...