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 }} \
# ... 12 more provider keys
- name: Set up monitoring
run: |
# Prometheus config, Grafana dashboards,
# alerting rules, log aggregation...
# This alone was 200+ lines of YAML
That's the happy path. We're not even talking about rollback strategies, canary deployments, or what happens when your agent starts hallucinating and burning through your API budget at 2x the normal rate.
We had an incident early on where an agent got stuck in a loop generating images. By the time we noticed, it had burned through about $400 in API calls in under an hour. That was our wake-up call.
Why OpenClaw
We evaluated a bunch of agent frameworks. Most of them wanted to own your entire stack — your prompts, your tool definitions, your execution model, everything.
OpenClaw was different. It's more like a protocol than a framework. You define your agent's capabilities, wire up your tools, and it handles the execution loop. But it's deliberately minimal about infrastructure opinions.
That minimalism is what attracted us, and also what made us realize there was a gap. OpenClaw gives you a great way to build agents. It doesn't give you a great way to run them.
What RapidClaw Does Differently
RapidClaw is basically the managed infrastructure layer that sits underneath your OpenClaw agents. Think of it as the platform that handles all the boring-but-critical stuff:
Deploy flow (what it looks like now):
┌─────────────┐ ┌──────────────┐ ┌─────────────────┐
│ Your Agent │────▶│ RapidClaw │────▶│ Production │
│ (OpenClaw) │ │ Platform │ │ Environment │
└─────────────┘ └──────────────┘ └─────────────────┘
│ │ │
│ ┌─────┴─────┐ ┌─────┴─────┐
│ │ Secrets │ │ Auto-scale │
│ │ Mgmt │ │ Monitor │
│ │ Isolation │ │ Cost caps │
│ │ Versioning │ │ Rollback │
│ └───────────┘ └───────────┘
│
rapidclaw deploy my-agent --env production
# That's it. One command.
The whole point is that you focus on your agent logic — what tools it has, how it reasons, what it's good at — and we handle the infrastructure. Secrets get injected securely, scaling happens automatically, and if your agent starts going off the rails, cost caps kick in before your cloud bill becomes a horror story.
You can dig into the security model if you want the details on how we handle isolation and secret management. It was one of the hardest parts to get right.
What We Learned (The Honest Version)
1. Agents fail in weird ways.
Traditional software fails predictably. API returns 500, you handle it. Database times out, you retry. Agents fail creatively. They'll find edge cases in your tools you never imagined. They'll interpret instructions in ways that are technically correct but completely wrong. Building good guardrails is less about error handling and more about understanding the problem space deeply enough to anticipate creative failures.
2. Cost management is a first-class concern.
This isn't like running a web server where your costs are roughly proportional to traffic. Agent costs can spike 10x in minutes if the agent decides it needs to "think harder" about something. We built per-agent budgets, per-session caps, and anomaly detection into the platform from day one. Should have done it from day negative-one.
3. Observability for agents is fundamentally different.
You can't just look at request/response logs. You need to see the agent's reasoning chain, understand why it chose one tool over another, and track how its behavior drifts over time. We built a trace viewer that shows the full execution tree — every tool call, every LLM interaction, every decision point. It's the feature our users care about most, and it was an afterthought in our original design. Embarrassing.
4. The open-source community taught us more than we expected.
We initially built RapidClaw as a purely internal tool. OpenClaw contributors kept asking us how we were running agents in production, and their questions shaped about 60% of our roadmap. Turns out the problems we were solving weren't unique to us — they were universal. That community feedback loop was the single most valuable thing in our development process.
5. You will underestimate state management.
Agents that run for minutes or hours need persistent state. They need checkpointing. They need the ability to resume after failures. And they need all of that without you having to think about it as an agent developer. Getting this right took us three complete rewrites. Three. We're still not 100% happy with it.
Where We Are Now
RapidClaw is running in production for a handful of teams. It's not perfect — our documentation needs work, our onboarding could be smoother, and there are definitely edge cases we haven't hit yet.
But the core loop works: write your OpenClaw agent, push it to RapidClaw, and it runs reliably in production with monitoring, scaling, and cost management built in. No more 200-line YAML files. No more 3am incidents because an agent went rogue.
If you're running OpenClaw agents (or thinking about it), I'd genuinely love to hear how you're handling the infrastructure side. We're at rapidclaw.dev/try if you want to kick the tires.
What's the gnarliest production issue you've hit with AI agents? I'll bet we've either seen it too or it'll end up on our roadmap. Drop it in the comments — I read every single one.
Top comments (0)