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In modern SRE, relying only on real-user monitoring means using production customers as unpaid QA. RUM is reactive, registering an outage only after traffic hits the error. Synthetic monitoring is proactive: it simulates complete end-to-end user journeys against live production apps on a recurring schedule.
By simulating critical business interactions like authentication, multi-tenant searches, cart additions, and payment processing, synthetic monitoring flags environment drift, certificate expirations, and third-party API degradation. It catches these problems before a real user ever encounters them.

Both run browser checks, but they serve different parts of the lifecycle. The distinction is about when each check runs and what kind of failure it is positioned to catch.
With Kane CLI you do not maintain two frameworks. The same plain-English objective that gates a PR drops into a GitHub Actions schedule, so the check that runs in CI/CD also runs your production monitor.
Note: Put your critical flows on a schedule and alert on the first failure. Start free. Try Kane CLI
Bundle your highest-value paths into an orchestration script so a failure on any journey triggers a non-zero exit for the whole run. Save it at monitoring/synthetic-monitor.sh.
#!/usr/bin/env bash
set -uo pipefail
FAILED=0
run_flow () {
local name="$1"
local objective="$2"
echo "::group::${name}"
kane-cli run "$objective" \
--headless --agent --timeout 300 \
--username "$LT_USERNAME" --access-key "$LT_ACCESS_KEY"
local code=$?
if [ "$code" -ne 0 ]; then
echo "FAILED: ${name} (exit ${code})"
FAILED=1
fi
echo "::endgroup::"
}
run_flow "login" "go to https://myapp.com, log in as '{{user}}', assert the dashboard shows 'Welcome'"
run_flow "search" "go to https://myapp.com, search for 'headphones', assert results appear"
run_flow "checkout" "go to https://myapp.com, complete checkout with a test card, assert the order confirmation page shows"
exit "$FAILED"When asserting against live production, use dedicated isolated test accounts and read-only queries where possible. This avoids polluting analytics or triggering real charges.
Write a scheduled workflow at .github/workflows/synthetic-monitoring.yml that runs every 15 minutes, provisions Chrome, and loads credentials from repository secrets. The schedule cron and the if: failure() alert step do the heavy lifting.
name: Synthetic Monitoring
on:
schedule:
- cron: '*/15 * * * *'
workflow_dispatch:
jobs:
monitor:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-node@v4
with:
node-version: '20'
- name: Install Chrome
uses: browser-actions/setup-chrome@v1
- name: Install Kane CLI
run: npm install -g @testmuai/kane-cli
- name: Run synthetic checks
env:
LT_USERNAME: ${{ secrets.LT_USERNAME }}
LT_ACCESS_KEY: ${{ secrets.LT_ACCESS_KEY }}
run: |
chmod +x ./monitoring/synthetic-monitor.sh
./monitoring/synthetic-monitor.sh
- name: Alert Slack on failure
if: failure()
env:
SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL }}
run: |
curl -s -X POST -H 'Content-type: application/json' \
--data '{"text":"Synthetic monitoring failed. Open the GitHub Actions run for the Kane CLI ShareLink."}' \
"$SLACK_WEBHOOK_URL"When a flow breaks, the script surfaces a non-zero exit, failing the step. The if: failure() step then fires an actionable Slack notification to the on-call channel.
Because Kane CLI captures video records and step-by-step logs for every run, the Actions log shows a secure ShareLink. On-call engineers click it to watch the failure replay, reducing MTTR.
Note: Want the agent mode and credentials setup behind this monitor? Read the Kane CLI docs. Read the docs
The same objective that runs every 15 minutes in production also runs before merge, the way Kane CLI handles a UI check before a PR ever lands.
Author
Anubhav Singhmaar is an AI Product Manager at TestMu AI driving Kane CLI, the command-line tool that brings browser automation to the terminal, turning natural-language flows into runs in a real Chrome browser that return pass or fail with shareable proof. He owns the roadmap and prioritization and works with engineering to ship developer-facing features. Before TestMu AI, he spent over four years at Sprinklr owning enterprise voice AI across APAC and EMEA. A mechanical engineer turned product manager, he grounds guidance in real QA workflows.
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