Runbooks and AI agents in code, with a person approving the risky steps

import { workflow } from "shiftlabs";

export default workflow<{ deployment: string }>(async ({ payload, step }) => {
  // Runs on your runner, inside your network.
  await step.exec("list pods", { command: ["kubectl", "get", "pods", "-n", "apps"] });
  const decision = await step.waitForApproval("restart", {
    message: `Restart ${payload.deployment}?`,
    approvers: ["role:admin"],
  });
  if (!decision.approved) return { restarted: false, note: decision.note };
  await step.exec("restart", {
    command: ["kubectl", "rollout", "restart", `deployment/${payload.deployment}`, "-n", "apps"],
  });
  return { restarted: true };
});

from shiftlabs import workflow


@workflow
async def restart(ctx):
    # Runs on your runner, inside your network.
    await ctx.step.exec("list pods", command=["kubectl", "get", "pods", "-n", "apps"])
    deployment = ctx.payload["deployment"]
    decision = await ctx.step.wait_for_approval(
        "restart",
        message=f"Restart {deployment}?",
        approvers=["role:admin"],
    )
    if not decision["approved"]:
        return {"restarted": False, "note": decision["note"]}
    await ctx.step.exec(
        "restart",
        command=["kubectl", "rollout", "restart", f"deployment/{deployment}", "-n", "apps"],
    )
    return {"restarted": True}