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Platform Engineer DevOps SRE CTO

Govern AI agents
on Kubernetes.

Gatekeeper, Falco, and native RBAC govern Kubernetes resources. None of them model the layer above that: an AI agent acting on behalf of a named developer, making decisions based on a prompt, calling a tool chain before any K8s API call is ever made. mogenius does.

KUBERNETES PLATFORM AI AGENT AI AGENT MCP POLICY GATE AUDIT LOG scale api-svc 3→8 ✓ delete ns/prod ✗ blocked exec pod/api-0 ⚠ approval sarah.k@dev namespace: staging ✓ identity attributed
The challenge

A new kind of builder.
A new kind of risk.

AI coding tools like Claude Code are sending a wave of builders into K8s clusters who are not infrastructure specialists. They command hundreds of agents in parallel. The impact amplification is real — and so is the damage potential.

Without mogenius
AI agents use service accounts — no developer identity attributed to agent actions
No governance at the MCP/tool-calling layer — only after K8s API call is made
Gatekeeper rejects manifests; it can't intercept agent intent before execution
No audit trail of what the agent was asked to do, why, or what it decided
A successful prompt injection can do anything the service account can do
With mogenius
Every agent action attributed: developer → agent → action → outcome
Governance fires before the K8s API call — preventive, not reactive
RBAC at the AI action level: contextual, identity-aware, intent-aware
Full prompt-to-action trace in the audit log — postmortem-ready
Injection blast radius constrained to what the authorised identity can do
How it works

In the execution path.
Not observing from outside.

The MCP Server + K8s Operator

The mogenius MCP server exposes the full Kubernetes toolchain to AI agents through a Model Context Protocol interface — governed by a purpose-built Kubernetes operator. Every tool call is validated against the policy for that identity and operation type before execution.

  • Developer identity attribution — developer → agent → action, fully traceable
  • Workspace scoping — context constructed at scope boundary, not filterable by prompt
  • Contextual RBAC — scale staging 09:00–18:00 only, max 10 replicas, with approval above 5
  • Structured context delivery — not raw YAML piped to LLM; reduces injection surface
  • Human-in-the-loop gates — configurable per operation type and namespace
Policy: dev/sarah.k — namespace: staging
deployments:scale✓ max 10 replicas
pods:logs✓ read
namespaces:delete✗ denied
pods:exec⚠ approval required
Live action log
14:32 scale api-svc 3→8✓
14:34 delete ns/prod✗ blocked
14:35 read logs/crash-0✓
Capabilities

What mogenius governs
that nothing else does

Capability Native K8s RBAC Gatekeeper / OPA Falco mogenius
Resource-verb access control✓✓✗✓
Developer identity attribution on agent actions✗✗✗✓
Governance before the K8s API call (preventive)✗✗✗✓
Contextual policy (time, environment, approval)✗✗✗✓
Prompt-to-action audit trace✗✗✗✓
Workspace isolation at context level✗✗✗✓
Runtime anomaly detection✗✗✓Soon

mogenius does not replace Gatekeeper or Falco — it governs the AI agent layer above them.

0→1
AI incident audit trail in Kubernetes — first of its kind
<1wk
Time to governed AI operations on any cluster
Any
LLM endpoint — hosted or self-hosted, no data egress required
100%
Actions attributed — developer → agent → outcome
Get started

Ready to govern your
AI agent layer?

Deploy in under a week. Talk to us about your current agent setup.