How to Scale OpenClaw Agents on Kubernetes with ACP and acpx

Running one OpenClaw agent on your laptop is easy. Running a hundred of them reliably, in parallel, across a production cluster — that’s a different problem entirely.

At an AI Engineer event, Onur Solmaz, OpenClaw’s core maintainer at Hugging Face, showed exactly how to do it. The talk, titled “Scaling Agents on Kubernetes with acpx and ACP,” is the most authoritative take on production-grade OpenClaw infrastructure available right now — and it introduces a Go-based Kubernetes operator that makes horizontal agent scaling significantly more tractable.

This article walks through the core architecture and concepts from that talk. For code specifics and the latest API, see the official docs below.

Why “Just Run More Containers” Doesn’t Work for Agents

It’s tempting to treat agent scaling like web server scaling: spin up more instances, put a load balancer in front, done. But AI agents are stateful in ways web servers are not.

An agent working on a task might:

  • Hold context about prior tool calls in its conversation history
  • Maintain an active session with an external tool or API
  • Be mid-execution on a long-running task that can’t be interrupted
  • Spawn child agents that need to communicate back to the parent

Standard container scaling patterns don’t account for any of this. You need an orchestration layer that understands agents as a distinct workload type — not just HTTP request handlers.

That’s where ACP and acpx come in.

ACP: The Agent Client Protocol

ACP (Agent Client Protocol) is a protocol that standardizes communication between AI agents and their orchestration systems. Think of it as the language agents use to talk to each other and to the infrastructure that manages them.

Solmaz described ACP as solving a critical interoperability problem: as agent ecosystems grow, you end up with agents built on different frameworks, running in different environments, needing to call each other and share context. Without a standard protocol, every connection becomes a custom integration.

With ACP, a Kotlin-based agent and a Python-based agent can communicate using the same protocol primitives. An orchestration system that speaks ACP can manage agents regardless of which framework built them.

Key concepts in ACP:

  • Sessions — stateful communication contexts that persist across multiple message exchanges
  • Capabilities — explicit declarations of what an agent can do, discoverable at runtime
  • Delegation — structured handoff of tasks between agents with context preservation

For the full ACP specification and current protocol documentation, refer to the official OpenClaw ACP docs.

acpx: The CLI for ACP

acpx is described by Solmaz as the “swiss army knife” for ACP management — a headless CLI that lets any agent or orchestration component call another agent over the command line using the ACP protocol.

The key insight: in a Kubernetes-native architecture, acpx becomes the glue that lets your Kubernetes operator (or any other automation) interact with running agent pods, check their status, submit work to them, and receive results — all using standardized ACP semantics rather than bespoke REST calls.

⚠️ Note: acpx is an evolving tool. Specific CLI flags and commands should be verified against the official OpenClaw documentation and the acpx repository. The architecture described here is accurate to Solmaz’s May 2026 AI Engineer talk.

The Kubernetes Architecture

Here’s the core architecture from the talk:

1. The Go-Based Kubernetes Operator

The heart of the system is a custom Kubernetes operator written in Go. This operator watches for agent task requests and responds by:

  1. Provisioning an isolated agent pod — a fresh pod per task, not a shared pool
  2. Wiring up tools — injecting tool configurations (Slack access, file system mounts, API credentials) into the pod at launch time
  3. Monitoring task completion — watching for the pod to finish its work
  4. Tearing down the pod — cleaning up the pod after the task completes, releasing resources

The pod-per-task isolation model is the critical design choice here. Each task gets a clean environment with no state contamination from prior tasks. This is more resource-intensive than sharing pods across tasks, but it gives you:

  • Security isolation — one agent’s compromised context can’t contaminate another
  • Predictable resource billing — each task has a known pod lifecycle
  • Clean failure modes — a crashed agent pod doesn’t affect other running tasks
  • Debuggability — you can attach to the pod for a specific task and inspect exactly what happened

2. Horizontal Scaling

Because each task gets its own pod, horizontal scaling becomes straightforward: the cluster’s node pool is your agent fleet capacity. Kubernetes’ standard autoscaling mechanisms (Horizontal Pod Autoscaler, Cluster Autoscaler) handle spinning up and down node capacity based on task queue depth.

Solmaz noted that the OpenClaw project itself processes “300-500 PRs per day on average” — this scale of concurrent activity is what drove the need for a properly orchestrated approach rather than manual agent management.

3. Tool Wiring

A significant challenge in agent infrastructure is getting tools into agents at runtime without hardcoding credentials. The operator handles this by injecting tool configurations as Kubernetes secrets or ConfigMaps into each pod at launch time.

For Slack integration, for example: the operator would inject the Slack token and channel configuration as environment variables into the agent pod. The agent code reads from standard environment variables rather than embedding credentials.

Best practice from the talk: Treat agent tool access like you’d treat database credentials in a 12-factor app — never hardcode, always inject from the environment. The Kubernetes operator is responsible for wiring the right credentials to the right task pods.

Getting Started

The best starting point is Solmaz’s talk itself, available on YouTube (youtube.com/watch?v=VaS2h-dY1-4). It includes live demonstrations of the operator in action.

For documentation:

For current and complete CLI syntax for acpx, always refer to the official documentation rather than examples that may have drifted from the current API.

Why This Architecture Matters

The pod-per-task isolation model, combined with ACP as a standard agent communication protocol, gives enterprise teams something they’ve been missing: infrastructure patterns for AI agents that follow the same rigor as any other production workload.

You can apply your existing Kubernetes RBAC, network policies, resource quotas, monitoring, and CI/CD pipelines to your agent fleet without inventing new paradigms. The operator abstracts the agent-specific complexity. Kubernetes handles the rest.

As agent workloads move from experiments to production, this is the kind of infrastructure story the ecosystem has needed. The OpenClaw maintainer shipping it as a talk with working code is a good sign for the state of the field.

Sources

  1. AI Engineer Talk — “Scaling Agents on Kubernetes with acpx and ACP” (YouTube)
  2. Onur Solmaz’s Personal Site — solmaz.io
  3. OpenClaw Official ACP Docs
  4. StartupHub.ai — Scaling AI Agents on Kubernetes with OpenClaw

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260521-2000

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