A deep dive into our Git-based workspace that combines knowledge management, multi-repository operations, and AI-native integration through MCP servers.
In 1985, NASA built a rule engine called CLIPS. Forty years later, it still has lessons for every developer whose business logic is buried in procedural code.
The principle everyone agrees on. The application everyone gets wrong. What if data and code were never separate concerns?
What happens when your data system has no idea what you're going to do with the answer.
What if conventional hiring has the priority order backwards?
Most rule engines failed to gain adoption because database integration was weak. In today's fragmented data landscape, getting data in and decisions out isn't optional. It's the foundation.
Temporal, DBOS, Windmill, and Lambda Durable Functions solve state durability. But code itself is the problem: workflows aren't sequences you build. They emerge from conditions. And code can't express emergence.
We ran experiments to find out why AI agents forget project-specific rules. The results were surprising: less is more, prohibition beats reframing, and recognition doesn't equal adherence.
Traditional databases follow a request-response pattern that leaves agents waiting, ignorant, and inactive. Agent-native systems flip this model.