Recent experimentation with AI agents has revealed a surprising phenomenon: convergent evolution in their design. Developers, working independently, encountered similar challenges—specifically, context degradation and agents trapping themselves in loops of false success—and arrived at remarkably similar architectural solutions, such as treating memory as a cache and using files as the single source of truth. This highlights the importance of robust architectures, like those incorporating dedicated "intent files" and a system for escalating discrepancies, as a framework like Legion prepares for a closed MVP release.
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