MCP-Native · Kubernetes-Aware · Human-in-the-Loop

Agentic DevOps
with SteadyHelm

AI agents that reason over live Kubernetes state and propose safe Helm operations — autonomously, with full audit trail and human approval gates.

Kubernetes Helm MCP Protocol OPA Policies eBPF Observability
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SteadyHelm Agentic Workflow

📊
Cluster Metrics
Prometheus / DCGM
📦
Helm Releases
Live state snapshot
🔔
Alerts / Events
OPA violations
🔗
MCP Server (SteadyHelm)
Structures cluster context into MCP-compatible tool calls
🧠
LLM Reasoning Engine
Gemini / GPT-4 — proposes upgrade, rollback, or scale action
Human-in-the-Loop Approval Gate · GitOps PR or Slack command
⚙️
Helm Upgrade
↩️
Rollback
📋
Audit Log

What is Model Context Protocol?

MCP is an open standard that gives LLMs a structured interface to read live system state and execute scoped tool calls — think of it as a type-safe API between your AI model and your infrastructure.

1
Expose Context

SteadyHelm's MCP server exposes Kubernetes resources — Deployments, HPA status, ConfigMaps, Helm release history — as typed MCP resources.

2
LLM Reasons

The LLM reads context and generates a proposed action (e.g. helm upgrade --set image.tag=v2.3.1 --atomic) with a reasoning trace.

3
Gate & Execute

OPA policy validates the action. A human approves via PR or Slack. SteadyHelm executes atomically and logs the full replay chain.

// SteadyHelm MCP tool call — cluster context snapshot
{
  "tool": "steadyhelm.cluster_state",
  "context": {
    "namespace": "production",
    "helm_releases": ["api-gateway", "model-serving"],
    "unhealthy_pods": 2,
    "hpa_utilisation": 94,
    "opa_violations": ["missing-resource-limits"]
  }
}

// LLM reasoning output →
{
  "action": "helm_upgrade",
  "release": "model-serving",
  "flags": "--set resources.limits.cpu=2 --atomic",
  "reasoning": "HPA at 94% — resource limits missing causing OPA violation. Patch fixes both.",
  "confidence": 0.91,
  "requires_approval": true
}▌

Why Agentic DevOps Wins

Standard automation reacts. Agentic infrastructure predicts and acts.

Cognitive Scaling

AI agents analyse external signals (traffic patterns, market events) and pre-scale Kubernetes clusters before load hits — not after CPU crosses 80%.

Policy-Gated Safety

Every proposed action passes OPA validation. Rollback commands are pre-computed and stored atomically so recovery is instant if anything drifts.

Intelligent FinOps

Agents correlate cost anomalies with deployment events. When a new Helm release spikes GPU spend, the agent flags it and proposes a right-sizing strategy automatically.

Full Audit Replay

Every agent decision — context, reasoning trace, approval, execution — is immutably stored. Replay any incident to understand the exact causal chain.

Pair-Programming Model

Warble Cloud embeds a senior Kubernetes Architect and an AI Platform Engineer on your team — they program the agents alongside your engineers, transferring knowledge continuously.

50% OPEX Reduction

Clients running Agentic DevOps see an average 50% reduction in infrastructure OPEX within 12 months via right-sizing, spot-instance optimisation, and automated incident remediation.

Enterprise Stack

Battle-tested tools, integrated into a single agentic platform

Core Platform
  • SteadyHelm — MCP Server for Helm + Kubernetes
  • Flux + ArgoCD — GitOps delivery engine
  • OPA / Gatekeeper — Policy-as-Code enforcement
  • Karpenter — Intelligent node provisioning
  • Cilium eBPF — Zero-trust networking & observability
AI / LLM Layer
  • Gemini / GPT-4 — Reasoning engine via MCP
  • LLM Gateway — Rate limiting, model routing, cost control
  • OpenTelemetry — Unified traces, metrics, logs
  • Prometheus + Grafana — Cluster telemetry & dashboards
  • OpenCost — Per-agent, per-namespace cost attribution

Scope Your Agentic Ops Project

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