Why arkonis-operator

If you are a solo developer experimenting with LLMs, you do not need this. Claude Code, the Anthropic API, or a simple Python script will serve you better.

arkonis-operator is for engineering teams running agents in production at scale, where the problems are operational, not algorithmic.

The problems it solves

“We have agents across multiple teams. How do we manage them?”

The same way you manage any other production workload: Kubernetes. Your platform team already knows the tooling, the access controls, and the deployment patterns. arkonis-operator plugs agents into that existing system instead of introducing a parallel management layer.

“An agent is burning through our API budget”

Hard limits enforced at the infrastructure level, not in application code that a developer can accidentally remove or misconfigure:

spec:
  limits:
    maxTokensPerCall: 8000
    maxConcurrentTasks: 5
    timeoutSeconds: 120

These are applied to every pod the operator creates, regardless of what the agent code does.

“How do we know if our agent is actually working?”

Standard Kubernetes liveness probes check whether a process is running. They cannot tell you whether an LLM is producing useful output.

arkonis-operator introduces semantic health checks: a secondary LLM call that validates the agent’s actual output quality. Pods that fail the semantic probe are removed from routing until they recover, exactly like a failing HTTP health check.

spec:
  livenessProbe:
    type: semantic
    intervalSeconds: 60
    validatorPrompt: "Reply with exactly one word: HEALTHY"

“We need to roll back our agent after a bad deploy”

A system prompt change is a pull request. A rollback is a sync revert in ArgoCD or Flux. The same GitOps workflow your team already uses for every other service works identically for agents.

“Compliance requires an audit trail of every agent configuration change”

Every agent definition goes through kubectl, RBAC, and git. Kubernetes already provides the audit trail: who changed what, when, and through which approval process.

“We need to chain agents together without writing orchestration code”

ArkonisPipeline defines multi-agent DAGs declaratively. Outputs from one agent are templated directly into the next step’s input:

spec:
  steps:
    - name: research
      arkonisDeployment: research-agent
      inputs:
        prompt: "Research this topic: "
    - name: summarize
      arkonisDeployment: summarizer-agent
      dependsOn: [research]
      inputs:
        prompt: "Summarize: "

No orchestration code. No custom queue logic. Just YAML.

Who this is for

Role Why it helps
Platform engineer Standard Kubernetes primitives for AI agents. No new management layer to operate.
Team lead Cost controls and limits enforced at infrastructure level, not per-developer discipline
SRE / on-call Semantic health checks and standard alerting work without custom tooling
Security / compliance RBAC, audit logs, and namespace isolation apply to agents automatically

Apache 2.0 · ARKONIS