What's the Git equivalent for AI agents?

Building with agents lately, and I've started wondering whether we're missing some of the engineering foundations that made traditional software manageable at scale.

In traditional software, state is straightforward. It's code, configuration, infrastructure. And Git handles that model well. In there, Diffs are clear, reviews are structured, and rollbacks are predictable. But agent systems feel different.

Once you move beyond simple demos, behavior starts depending on prompts, tool access, memory, retrieval pipelines, model versions, MCP servers, and runtime decisions. A significant part of what the system actually does isn't represented in a single place where ou can inspect, review, or reproduce. Should prompts, tools, memory definitions, and evaluation criteria be treated as first-class versioned assets? Should an agent have a declarative specification that's reviewable and rollbackable in the same way application code is? Or does that framing itself not quite fit?

GitHub Next's Agentic Workflows are already exploring similar ideas. GitAgent already exists. Claude Code and Codex both lean heavily on git based workflows. There's a growing belief that agent behavior should be something we can version, review, and deploy using the engineering workflows we already trust.

Maybe that's the right direction. Maybe agents end up needing a completely different set of abstractions. I'm curious how people building production systems are thinking about it. How are you handling reproducibility, change management, and operational visibility today?