The enterprise stack evolved in three distinct architectural layers, each attempting to solve the limitations of its predecessors, but ultimately creating new technical debt that we're still grappling with today.
The foundation began with Systems of Record in the 1980s. Built on relational databases with CRUD interfaces, these systems forced unstructured human workflows into rigid schemas. The resulting architecture optimized for storage rather than usage patterns, creating massive data models that proved increasingly difficult to evolve as business needs changed.
Systems of Engagement emerged in the 2000s as a solution to the interface problem, but ironically created a more fundamental data architecture crisis. While they successfully made software more intuitive, they generated exponentially more unstructured data across text, audio, and video. The architectural disconnect between this unstructured data and the underlying structured data models created a chasm that still hasn't been bridged.
The industry's response came in 2017 with Systems of Intelligence, attempting to bridge the structured/unstructured divide. However, these systems suffered from a fatal architectural flaw: they remained dependent on manual data entry at the source. Unable to solve the sparse data problem in enterprise contexts, they failed to generate meaningful value at scale. Despite massive investment, the space has yet to produce a billion-dollar revenue company.
The architectural revelation is that instead of building more abstraction layers, we need autonomous agents that collapse the stack entirely. This means fundamentally reimagining how enterprise systems handle information flow. Data capture moves to the source, eliminating manual entry points. Natural language becomes the primary interface, and the structured/unstructured divide disappears as agents handle translation in real-time. Traditional UI patterns become obsolete in this new paradigm.
Today founders are rebuilding enterprise systems from first principles around autonomous agents that understand, decide, and execute in real-time. For technical founders and architects working on these problems, the challenge isn't just technical implementation but rethinking the fundamental assumptions about how enterprise software should be structured.
Curious to hear from other founders working on this: What architectural decisions are you making as you rebuild these systems from first principles?
Post in the comments!
