Why We Built a Managed Platform for OpenClaw Agents (And What We Learned)

We spent six months wrestling with deploying AI agents before we decided to just build the thing ourselves. This is that story — the ugly parts included. The Problem Nobody Talks About Everyone’s building AI agents right now. The demos look incredible. You wire up some tools, connect an LLM, and suddenly you’ve got an agent that can research, plan, and execute tasks autonomously. Then you try to put it in production. Suddenly you’re dealing with container orchestration, secret management, scaling workers up and down, monitoring token spend, handling failures gracefully, and figuring out why your agent decided to retry the same API call 47 times at 3am. We were building on OpenClaw — an open-source agent framework that we really liked because it didn’t try to do too much. It gave you the primitives and got out of the way. But “getting out of the way” also meant we were on our own for everything else. What Running Agents in Production Actually Looks Like Here’s a simplified version of what our deploy pipeline looked like before RapidClaw existed: # Our old “deploy an agent” workflow (simplified, but not by much) steps: – name: Build agent container run: docker build -t agent-${{ agent.name }} . – name: Push to registry run: docker push $REGISTRY/agent-${{ agent.name }} – name: Update k8s deployment run: | kubectl set image deployment/$AGENT_NAME agent=$REGISTRY/agent-${{ agent.name }}:$SHA – name: Configure secrets run: | kubectl create secret generic agent-secrets –from-literal=OPENAI_KEY=${{ secrets.OPENAI }} –from-literal=ANTHROPIC_KEY=${{ secrets.ANTHROPIC }}…

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