Docker Is Building the Plumbing for Agentic AI

I've been following the AI conversation as it evolves from "assistants" (generative tools you prompt one task at a time) to autonomous "agents" that can own an entire process end to end. The idea is genuinely powerful. But the question I keep coming back to is the practical one: how does a company reliably build, deploy, and scale thousands of these agents? It's one thing to make a cool demo. It's a completely different thing to make it a stable business tool.

It reminds me of the scene in The Founder about the McDonald's brothers. They didn't win because they had a good hamburger. They won because they had an innovative system, the "Speedee System," that they famously mapped out on a tennis court. The hamburger was never the real innovation. The repeatable, efficient process behind it was.

That's why Docker's recent moves caught my attention more than another flashy model release would have. By extending Docker Compose to support agents and adding features like Docker Offload, Docker is building the standardized plumbing underneath all of them, not another agent. It's figuring out the "kitchen workflow" so that building and deploying an agent becomes repeatable, reliable, and secure by default, the same way it did for microservices a decade earlier. That's the unglamorous, infrastructural work that's actually a precondition for agentic AI moving from a tennis-court prototype into a real enterprise tool teams can depend on without babysitting it.

Watching this foundational layer get built is a useful reminder for anyone building on top of agents, myself included: the model is rarely the bottleneck for very long. The system around it (deployment, observability, retries, security boundaries) is what decides whether an agent survives contact with a real production environment. This move toward standardized agentic infrastructure feels like the step that quietly redesigns how businesses actually operate, well before most of the public discourse catches up to it.