At Catio, we've embraced a remote-first approach from day one, joining other forward-thinking startups in pioneering a new way of working. Since our inception in December 2022, we've embraced the power of distributed teams, leveraging technology and innovative practices to create a dynamic, flexible work environment.
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.
Choosing the right API paradigm is crucial for any application's success. At Catio, after much deliberation and analysis, we decided on GraphQL for our Catio Console.
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.