Self-Hosted AI Agent for Kubernetes & CI/CD

   

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Infrastructure automation tools fall into two categories.

CLI assistants translate prompts into shell commands. Autonomous agents execute infrastructure changes without explicit approval.

Neither model guarantees a deterministic execution process or a complete audit trail. And neither one remembers what happened last time.

Skyflo is a self-hosted AI agent for Kubernetes and CI/CD systems. It runs inside your cluster and executes infrastructure operations through a deterministic control loop:

Recall → Plan → Approve → Execute → Verify → Persist

Every mutating tool call is approval-gated, typed, and auditable. Every confirmed incident lesson is saved and recalled on the next relevant task.

Skyflo is not a CLI wrapper, not an autonomous mutation bot, and not a GitOps control plane.

It is an in-cluster AI control layer that enforces safe infrastructure changes before anything reaches production -- and builds operational memory across every session.

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Quick Start

Install Skyflo inside your Kubernetes cluster.

Using Helm:

helm repo add skyflo https://charts.skyflo.ai
helm repo update skyflo

Create a values.yaml file:

engine:
  secrets:
    llmModel: "gemini/gemini-2.5-pro"
    geminiApiKey: "AI-..."

See helm show values skyflo/skyflo for the full list of configurable values.

helm install skyflo skyflo/skyflo -n skyflo --create-namespace -f values.yaml

Using curl:

Get started quickly with the interactive installer.

curl -fsSL https://skyflo.ai/install.sh | bash

Bring your own LLM (OpenAI, Anthropic, Gemini, Groq, self-hosted). See the quick start guide.

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Execution Model

Skyflo enforces a strict loop for every infrastructure change:

1. Recall: retrieve prior incidents, runbooks, and service context from scoped memory
2. Plan: generate a concrete, replayable plan
3. Approve: explicit approval for every mutating tool call
4. Execute: run typed tools via MCP (Kubernetes, Helm, Argo Rollouts, Jenkins)
5. Verify: validate cluster state against declared intent
6. Persist: store tool-level audit history and save confirmed lessons to memory

No blind kubectl apply. No silent automation. No untracked changes.

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Safety Properties

• Approval gate for every mutating tool call, enforced by the engine
• Typed tool execution with schema-validated inputs
• Persisted audit trail with tool results
• Replayable control loop (recall → plan → approve → execute → verify → persist)
• Scoped memory with safety scanning (blocks secrets, raw logs, prompt injection)
• Runs inside your cluster. No Skyflo telemetry or phone-home
• LLM-agnostic via LiteLLM. No vendor lock-in

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Supported Tools

Tool	Capabilities
Kubernetes	discovery, get/describe, logs/exec, diff-first apply, rollout history, rollbacks
Helm	template, install/upgrade/rollback, dry-run, diff-first safety
Argo Rollouts	status, pause/resume, promote/cancel, progressive delivery control
Jenkins	jobs/builds/logs, parameters, SCM context, build control

All mutating tool calls require explicit approval.

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Demo

Deterministic plans. Explicit approval. Verified execution.

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Comparison

Capability	CLI Assistants	Autonomous Agents	GitOps Platforms	Skyflo
Natural language ops	Yes	Yes	Limited	Yes
Mandatory mutation approval	Optional	No	PR-based	Yes
Deterministic control loop	No	No	Partial	Yes
Kubernetes + CI unified	No	Partial	No	Yes
In-cluster deployment	Partial	Partial	Varies	Yes
Team RBAC + audit	No	Limited	Yes	Yes
Real-time execution streaming	No	No	No	Yes

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System Architecture

Component	Description
Engine	LangGraph workflow: memory retrieval, planner, approval gate, verifier, persistence, auth/RBAC
MCP Server	Typed tools for Kubernetes, Helm, Argo Rollouts, Jenkins
Command Center	Next.js UI with real-time streaming, approvals, memory context panel, team admin

Details: Architecture

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Contributing

Apache 2.0 OSS. High-signal contributions welcome. See CONTRIBUTING.md.

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License

Apache 2.0. See LICENSE.

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Community

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