Architecture, Deconstructed

AI is now a fixture of the modern engineering stack. It writes our code, summarizes our meetings, and drafts our documentation. But beneath the surface, something isn’t clicking.

Architecture diagrams lie. The new Physical-First model in Catio Stacks shows you how your infrastructure actually runs, anchored in real-world zones, not design assumptions.

How human-centered design principles are shaping the future of AI product development

GraphQA is Catio’s new open-source agent for natural-language questions over architecture graphs, fusing LLMs with graph algorithms to deliver fast, structure-aware answers for dependencies, flows, and system reasoning.

When we begin to treat decisions as measurable, we open the door to a new class of metrics that finally support the decision brain as much as the operational one. Because even the most elegant microservice doesn’t help if it was the wrong service to build.

Our engineers and product leads are already using AI to improve the way we design, build, and ship software across architecture, integration, planning, and execution. This post captures how five of us use AI to move faster, improve quality, and preserve control.

A major upgrade to Catio’s recommendation engine, now with business impact scoring built in. Teams can instantly prioritize architecture decisions by cost, complexity, and strategic value for faster, smarter results.

In our first episode of Architecture Deconstructed, we sat down with two seasoned engineering leaders - Satish Raghunath, who leads infrastructure engineering at Salesforce, and Emil Ibrishimov, head of engineering, product, and design at recruiting-tech startup Gem.

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.

How Certificate Hero optimized AWS infrastructure costs and gained operational clarity with Catio’s AI-powered tech stack management platform—boosting performance and reducing reliance on DevOps expertise.

How Catio’s multi-agent recommendation engine brings AI-native intelligence to enterprise architecture. Learn how orchestrated AI agents deliver contextual, data-driven design insights across your tech stack.

We’re thrilled to announce that we’ve raised a $3M round to fuel our mission of transforming tech architecture decision-making with AI-driven intelligence.

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 post will guide you through the complexities of architecture visualization. We'll explore four key areas: Graphs, Hierarchy, Layout, and Context. By the end, you'll have a comprehensive understanding of the challenges in architecture visualization and be better equipped to create effective diagrams.

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.

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.

Welcome to a brief “Behind the Scenes” tour of how we on the Catio engineering team designed and built the Catio Console.

If you’re a techie like me, and have lived through the evolution of Neural Networks and Machine Learning into Deep Learning in the last couple of decades, you have to admit that we live in magical times!

We are thrilled to announce the recent closing of our oversubscribed Series PreSeed funding round, adding an additional $2.6M to our initial $1.4M raise earlier in 2023. Our funding was led by...

Over the last few decades, the fields of Machine Learning and Artificial Intelligence have evolved from their basis in statistics and simple Neural Networks into complex decision-making systems. When I was...

We’re beyond thrilled to share a defining milestone at Catio — the formation of our Co-founding Team! Bringing together a world-class team has been our number one focus, to cement the foundation for...

There’s no doubt that the rise of LLMs, and most importantly them evolving to a level of capability now sufficiently valuable and reliable for core use cases, has turned the world upside down. There has also...

KubeCon / CloudNativeCon was full of energy, with an entire ecosystem collaborating, even if they are competitors, to improve...

As the cloud native services landscape erupts (1,200 providers now, with $19T in enterprise value, and 23 companies and growing with over $1B in revenue), so does the appetite for cloud-...