About Systems
Over decades of industry development, a large body of knowledge has been accumulated about building software systems. There are patterns, practices, and approaches that work. But each system remains unique, with its own context and challenges.
When you work with real systems, you quickly realize: most of the time you don’t start with a clean slate. You start with history, compromises, half-implemented ideas, and changing priorities. And that’s normal.
A New Era of Complexity
With the emergence of AI agents, system complexity is growing exponentially. Now we’re dealing not just with deterministic algorithms, but with autonomous agents that make decisions based on probabilities and context. This changes everything: from testing approaches to monitoring and debugging methods.
Systems are becoming more dynamic and unpredictable. Classical architecture patterns remain important, but now they need to be complemented with new approaches to managing uncertainty and observing agent behavior.
Good architecture starts with understanding context, not applying ready-made solutions. And today this context includes working with AI agents and managing exponentially growing complexity.
