The Determinism Dilemma: Why Traditional Software Engineers Struggle with AI Development

The goal of creating something "predictable," reliable, and consistent is a shared principle across all the teams I've worked with throughout my career.

Knowing that the same code would always return the same output when given the same inputs was the foundation of everything we built.

We aimed for no surprises, no matter how complex a workflow might be. Whether implicitly or explicitly using finite state machines, this determinism enabled us to build testable, monitorable, maintainable, and, most importantly, predictable workflows.

We read and shared ideas at conferences, promoting patterns and principles like SOLID and DRY to create functional, composable, and extensible software.

Déjà Vu: AI Agent Frameworks and the JavaScript Gold Rush Revisited

Having lived through the era of a "new JavaScript framework every week," we now find ourselves in the gold rush of the AI agent framework space.

New frameworks appear daily, each claiming to be the 'ultimate' solution for building AI agents, often backed by YouTubers enthusiastically promoting demoware and usually their own library, framework, or SaaS offering. Unfortunately, this enthusiasm can lead companies to uncritically adopt these tools without considering the long-term implications.