Agent = Docker Container: A mental model that simplified everything in my agent infra project

As an AI algorithm researcher and engineer, I recently started building infrastructure for next-gen agents and agentic systems. The water is much deeper than I originally expected. Here's one insight worth sharing: agent export/import.

I initially built on OpenClaw, where the "workspace" felt like a natural unit for agent export/import. But as development progressed, hard questions emerged:

• How do you let users curate public data from private data before exporting?
• Given a workspace, how do you import it into a new environment with the agent's functionality intact?
• What about secrets, API keys, and environment-specific configs?

These became significant blockers.

Then a few days ago, my team started building agent architectures parallel to OpenClaw — and it hit me: in the future, everyone will build their own agent runtime. OpenClaw is great, but it's not the only destination. So why couple my infrastructure to one runtime?

I pivoted to building an abstraction layer — infrastructure that's agnostic of the agent runtime.

And that's when a powerful mental model clicked:

Agent = Docker container
Agent export = docker commit + docker push
Agent import = docker pull + docker run
Agent inventory = Docker registry

Simple. No need to reinvent the wheel. Docker already solved packaging, versioning, distribution, and isolation decades ago. An agent is just a container with a personality.

This reframing unlocked everything — marketplace, versioning, rollback, multi-runtime support — all using battle-tested container tooling.

My question to this community: What do you think is the right abstraction model for agents? Is "agent = container" too simplistic, or is that exactly the kind of primitive we need?