#agent-runtime

In eight weeks, four companies shipped agent runtimes targeting the same architectural pattern. OpenClaw went from 9K to 68K GitHub stars. Perplexity launched Computer. Anthropic launched Dispatch. NVIDIA announced NemoClaw at GTC. Different audiences. Different tradeoffs. Same structural claim: agents need a long-running runtime with containment boundaries, not a chat window. The barrier to deploying an autonomous agent is now lower than the barrier to writing a containment policy for one. Every company needs to treat this as infrastructure, not a trend.

We deployed OpenClaw using Claude Code and a work-system that enforces stage-based governance. Two architectural spikes before any code. An epic with three features, six stories, and explicit acceptance criteria. TDD throughout. Budget daemons with three-tier enforcement. The interesting part is not the deployment. It is the separation. The agent that built the runtime never ran inside it. The system that writes the budget daemon does not have its spending governed by that daemon. That separation is not an accident. It is containment architecture.

OpenClaw is usually described as an AI assistant, but that description misses what it actually is. It is not just a chat interface or helper sitting on top of a model. It is a harness, an agent runtime that connects a language model to tools that can read files, execute shell commands, and call APIs. The right mental model is not “install an AI assistant.” The right mental model is “deploy an autonomous process with the ability to operate on my machine.” That shift matters because it changes how you evaluate risk and how you design the system around it. Once you see it that way, the problem is no longer installation. It is containment. You are not asking how to run it. You are asking how to control it. That is where structure comes in. Three isolation layers, five containment rings, and a core principle that governs everything. The environment says “can’t,” not “shouldn’t.”