"Software engineering didn't adopt AI agents faster because engineers are more adventurous, or the use case was better. They adopted them more quickly because they already had Git. Long before AI arrived, software development had normalized version control, branching, structured approvals, reproducibility, and diff-based accountability. These weren't conveniences. They were the infrastructure that made collaboration possible. When AI agents appeared, they fit naturally into a discipline that already knew how to absorb change without losing control."
"What these disciplines need is not a literal code repository, but a shared operational substrate: a canonical artifact, fine-grained versioning, structured workflows, and an agreed-upon way to propose, review, approve, and audit changes. Consider a simple example. Imagine a product marketing team using an AI agent to maintain competitive intelligence. The agent gathers information, synthesizes insights, and updates a master brief used by sales and leadership. This seems straightforward-until the agent edits the document."
Software development normalized version control, branching, structured approvals, reproducibility, and diff-based accountability long before AI. These features functioned as infrastructure enabling collaboration, isolation of change, review workflows, reproducible versions, and audit trails. AI agents integrated into software practices because the existing backbone enforced isolation and accountability. Other disciplines that adopt AI agents without a similar backbone face instability rather than compounding gains. A suitable backbone is a shared operational substrate: a canonical artifact, fine-grained versioning, structured workflows, and agreed methods to propose, review, approve, and audit changes. Without such mechanisms, agents can overwrite content, obscure diffs, eliminate audit trails, and frustrate reversion.
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