AI isn’t just a feature, it’s a new architectural layer. In this deep dive, we explore emerging architecture patterns for the AI-native enterprise, from agentic systems to LLM-as-interface, and what they mean for CTOs and architects designing for adaptability.

Every major technology arrives with a wave of fear and uncertainty, but also with opportunities to redefine what skilled, meaningful work looks like. AI is just the latest participant in this long-running story, continuing the cycle of adjustment and growth that has always accompanied new tools.

This blog introduces a specialized chat service designed to meet the unique demands of multi-agent systems, with a particular focus on Catio's innovative approach to system architecture recommendations.

Or is it AI supercharging Multi-Agent Systems (MAS)? In the fast-evolving world of AI, the challenge of reasoning — how machines can solve problems with deep understanding and adaptability — is becoming more critical. From cutting-edge language models to intelligent agents, the race is on to unlock AI's full potential. At Catio, we've been exploring how AI can transform our processes, and we've placed our bets on MAS to push AI reasoning to the next level.

The Multi-Agent approach to solving problems is seeing a revival lately, but has been around for quite some time and has some very sound theoretical underpinnings. In this article I wanted to provide some fundamentals into the MAS approach, as a precursor to additional articles being planned by my Catio team to expand on the MAS work being done internally.