Kubernetes SRE Orchestration Quickstart

Deploy an AI-powered Kubernetes SRE agent that uses orchestration mode to coordinate specialized workers — one for cluster inspection, one for metrics analysis — each with access to only the tools they need.

What You’ll Build

                         User Query
                             |
                             v
          +------------------------------------------+
          |        AURA (Coordinator)                 |
          |  Routes requests to the right specialist  |
          |                                           |
          |  +-------------------+ +----------------+ |
          |  | cluster_inspector | | metrics_analyst| |
          |  | K8s tools only    | | Prom tools only| |
          |  +--------+----------+ +-------+--------+ |
          +-----------|--------------------|----------+
                      |                    |
                      v                    v
              +---------------+   +-----------------+
              | K8s MCP       |   | Prometheus MCP  |
              | Server        |   | Server          |
              +-------+-------+   +--------+--------+
                      |                    |
                      v                    v
              +---------------+   +-----------------+
              | Kubernetes    |   | Prometheus      |
              | API           |   | (OTel Demo)     |
              +---------------+   +-----------------+

The coordinator receives user queries and dispatches them to:

cluster_inspector — filtered to Kubernetes MCP tools (pods, deployments, logs, events)
metrics_analyst — filtered to Prometheus MCP tools (PromQL queries, alerts, targets)

Prerequisites

Docker
kubectl
kind
Helm 3.12+
An OpenAI API key (or another supported LLM provider)

Setup

All commands assume you’re in the repo root.

1. Create a KIND cluster

kind create cluster --name aura-sre

2. Deploy the OpenTelemetry Demo

The OpenTelemetry Demo deploys a microservices application with Prometheus, Grafana, and Jaeger — giving your SRE agent real workloads and metrics to inspect.

helm repo add open-telemetry https://open-telemetry.github.io/opentelemetry-helm-charts
helm install otel-demo open-telemetry/opentelemetry-demo

Wait for pods to come up (this takes a few minutes on first pull):

kubectl get pods -w

Not every pod needs to be Running before proceeding. As long as the Prometheus pod is ready, you can continue.

Verify Prometheus is running and note the service name:

kubectl get svc | grep prometheus

You should see a service like prometheus on port 9090. Note the name — if it differs, use it in the --set flag in step 3 below.

3. Deploy the MCP servers

Both MCP servers have community Helm charts. Install them with:

# Kubernetes MCP Server — read-only cluster access
# Binds the built-in "view" ClusterRole for read access to cluster resources.
helm install kubernetes-mcp-server \
  oci://ghcr.io/containers/charts/kubernetes-mcp-server \
  --set ingress.enabled=false \
  --set config.read_only=true \
  --set 'rbac.extraClusterRoleBindings[0].name=view' \
  --set 'rbac.extraClusterRoleBindings[0].roleRef.name=view' \
  --set 'rbac.extraClusterRoleBindings[0].roleRef.external=true'

# Prometheus MCP Server — connected to the OTel Demo's Prometheus
# Override probes to use TCP (the MCP server has no GET health endpoint).
helm install prometheus-mcp-server \
  oci://ghcr.io/pab1it0/charts/prometheus-mcp-server \
  --set prometheus.url="http://prometheus:9090" \
  --set livenessProbe.httpGet=null \
  --set 'livenessProbe.tcpSocket.port=http' \
  --set readinessProbe.httpGet=null \
  --set 'readinessProbe.tcpSocket.port=http'

Different Prometheus service name? Run kubectl get svc | grep prometheus and replace the URL above with the correct service name from step 2.

Verify both MCP servers are healthy before proceeding:

# Check the Kubernetes MCP server can reach the API
kubectl logs -l app.kubernetes.io/name=kubernetes-mcp-server --tail=5

# Check the Prometheus MCP server connected to Prometheus
kubectl logs -l app.kubernetes.io/name=prometheus-mcp-server --tail=5

Wait for them to start:

kubectl wait --for=condition=ready pod -l app.kubernetes.io/name=kubernetes-mcp-server --timeout=120s
kubectl wait --for=condition=ready pod -l app.kubernetes.io/name=prometheus-mcp-server --timeout=120s

4. Deploy AURA

export OPENAI_API_KEY="sk-..."

helm install aura ./deployment/helm/aura \
  -f examples/quickstart-k8s-sre/aura-values.yaml \
  --set secrets.openaiApiKey="$OPENAI_API_KEY"

Using a different LLM provider? Edit aura-values.yaml and update the [agent.llm] section. See examples/reference.toml for all provider options.

Wait for AURA:

kubectl wait --for=condition=ready pod -l app.kubernetes.io/name=aura --timeout=120s

5. Try it out

The AURA CLI ships inside the same image as the server, so there’s no separate CLI container to deploy. Exec into the running AURA pod and launch the bundled CLI against the in-pod server:

kubectl exec -it deploy/aura -- ./aura --api-url http://localhost:8080 --model kubernetes-sre

This drops you into an interactive REPL. Try these queries:

Check the health of my cluster. Are all pods running? Are there any high CPU or memory usage concerns?

What services are running in the default namespace and what are their error rates?

Are there any pods in CrashLoopBackOff? If so, show me their logs and related metrics.

Show me the top 5 pods by memory usage and check if any are close to their limits.

The coordinator dispatches to both workers: cluster_inspector checks pod status and events, while metrics_analyst queries Prometheus for resource usage. You can toggle the SSE event panel with /stream to watch the orchestration in real time. Type /quit to exit.

Prefer a browser or a local CLI? Port-forward the service with kubectl port-forward svc/aura 8080:80, then point any OpenAI-compatible client — or a locally built aura --api-url http://localhost:8080 — at it.

How the orchestration config works

Open aura-values.yaml and look at the config.content section. The key pieces: [orchestration] — enables orchestration mode. The coordinator agent receives every query and decides whether to answer directly, ask for clarification, or dispatch to workers. [orchestration.worker.cluster_inspector] — a worker with mcp_filter set to only Kubernetes tool names. Even though both MCP servers are connected, this worker can only see and use K8s tools. [orchestration.worker.metrics_analyst] — a worker with mcp_filter set to only Prometheus tool names. It can only query metrics, not touch the cluster. This separation means each worker operates with least-privilege access to tools, and the coordinator handles synthesis across domains.

Customizing tool filters

The mcp_filter arrays in aura-values.yaml list the exact tool names exposed by each MCP server. The names in this quickstart were sourced from the upstream repos:

kubernetes-mcp-server tools — core toolset, read_only = true
prometheus-mcp-server tools — all tools

To verify the tools AURA actually discovered at runtime:

kubectl logs -l app.kubernetes.io/name=aura | grep -i "tool"

If a tool name in mcp_filter doesn’t match any real tool, it’s silently ignored. If none match, the worker has zero tools and will fail.

Cleanup

helm uninstall aura
helm uninstall prometheus-mcp-server
helm uninstall kubernetes-mcp-server
helm uninstall otel-demo
kind delete cluster --name aura-sre

Getting Started

Configuration

Features

CLI Reference

Web Server Reference

Kubernetes SRE Orchestration Quickstart

What You’ll Build

Prerequisites

Setup

1. Create a KIND cluster

2. Deploy the OpenTelemetry Demo

3. Deploy the MCP servers

4. Deploy AURA

5. Try it out

How the orchestration config works

Customizing tool filters

Cleanup

​What You’ll Build

​Prerequisites

​Setup

​1. Create a KIND cluster

​2. Deploy the OpenTelemetry Demo

​3. Deploy the MCP servers

​4. Deploy AURA

​5. Try it out

​How the orchestration config works

​Customizing tool filters

​Cleanup

What You’ll Build

Prerequisites

Setup

1. Create a KIND cluster

2. Deploy the OpenTelemetry Demo

3. Deploy the MCP servers

4. Deploy AURA

5. Try it out

How the orchestration config works

Customizing tool filters

Cleanup