Source: Dev.to | Dec 31, 2025 (UTC)
Cloud and DevOps work is not hard because the commands are hard.
It is hard because your context is fragmented.
- Terminals, dashboards, logs, CI, rollouts
- Different auth models per system
- Different failure modes per workflow
- No shared audit trail of what happened and why
Skyflo.ai is my attempt to reduce that fragmentation.
Skyflo is an open-source AI agent for Cloud and DevOps.
It unifies Kubernetes operations and CI/CD behind a natural language interface, with approvals built in.
The important part is not “chat with prod”.
The important part is control.
Skyflo is a command center, an agent engine, and a standardized tool layer.
- Command Center (UI): a chat interface that streams every step in real time
- Engine: a service that runs a LangGraph workflow and turns intent into safe tool calls
- MCP Server: a tool server exposing standardized integrations for Kubernetes and CI/CD
You express intent in plain English.
What executes is always a validated tool call.
Skyflo is not “give an LLM kubectl and pray”.
If you want autonomous mutation in production without approvals, you are optimizing for demos.
You are not optimizing for reliability.
Skyflo is designed for operators who want automation without losing control.
- SREs
- DevOps engineers
- cloud architects
- platform teams
- security minded operators
Skyflo is built around a simple policy:
The agent can propose and prepare. You approve mutations.
That policy shows up in four places.
1) Human in the loop for mutations
Any WRITE operation requires explicit approval.
Examples:
-
apply, rollout promote or cancel - Helm upgrade or rollback
- actions that stop or cancel builds
Read only workflows can run end to end.
This is the only loop that matters in operations.
Skyflo runs an iterative workflow:
- Plan: interpret intent and perform lightweight discovery when needed
- Execute: call tools, then pause for approval if the next step is a write
- Verify: validate outcomes against intent, then continue or stop
3) Everything streams in real time
Operators do not trust black boxes.
Skyflo streams what it is doing as it does it:
- model output
- tool progress
- tool results
- workflow events
- approvals and decisions
This is operational safety.
It is also how you debug the agent.
4) Standardized tool execution via MCP
Integration work is where most agent projects fail.
Skyflo uses MCP so tools are:
- discoverable
- typed
- validated
- documented
- separable from the agent logic
The Engine does not know kubectl.
It knows tools.
Skyflo ships with standardized tools for:
- Kubernetes: discovery, get and describe, logs and exec, safe apply and diff flows
- Argo Rollouts: status, pause and resume, promote and cancel, progressive delivery visibility
- Helm: search, install, upgrade, rollback with dry run and diff first safety
- Jenkins: jobs, builds, logs, SCM context, identity, secure auth, CSRF handling
Write operations always require approval.
These prompts map to real on call muscle memory.
Show me the last 200 lines of logs for checkout in production. If there are errors, summarize them. Then check if any rollout is in progress.
What you should see:
- discovery of namespace, deployment, and pods
- logs and events
- rollout state inspection
- a structured summary
Progressive delivery with guardrails
Canary rollout auth-backend in dev through 10/25/50/100 steps. Pause at 25% if error rate increases.
What you should see:
- a rollout plan and read only checks first
- an approval gate before any mutation
- verification after each step
Pull logs for the last failed build of job X. Extract the first failing stage and tell me what changed since the last green build.
What you should see:
- build discovery
- log retrieval
- structured extraction
- a concrete next step
Install Skyflo.ai into a Kubernetes cluster:
curl -sL https://raw.githubusercontent.com/skyflo-ai/skyflo/main/deployment/install.sh -o install.sh chmod +x install.sh ./install.sh
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