Reliability Testing with Chaos Monkey + Rootly: Playbook

Chaos Engineering is the discipline of experimenting on a system to build confidence in its ability to withstand turbulent conditions in production. The practice was pioneered by Netflix with Chaos Monkey, a tool that randomly terminates production instances to test for resilience. But running chaos experiments is only half the battle. The other half involves managing, tracking, and learning from the incidents these experiments create.

This is where an incident management platform becomes essential. By integrating chaos experiments with a platform like Rootly, you can structure the response to these controlled failures. This integration turns potential chaos into a valuable, repeatable learning opportunity for your Site Reliability Engineering (SRE) teams.

The Foundation: Understanding Chaos Engineering and Incident Response

Before diving into the playbook, it's crucial to understand the two core components: the experiment itself and the response to it.

What is Chaos Engineering?

Chaos Engineering is a proactive approach to identifying system weaknesses before they cause widespread outages. It involves intentionally injecting failures—like network latency, CPU spikes, or terminating virtual machines—to observe how the system behaves under stress [7]. The primary goal isn't to break things; it's to find and fix failures before they become customer-facing incidents, thereby building more resilient systems.

Why Incident Response is the Critical Other Half

A chaos experiment without a structured incident response plan is just breaking things without a clear path to learning. The true test of reliability includes not just how the system technically responds to failure, but also how the human team responds.

Each experiment's fallout should be treated like a real incident to test and refine communication channels, escalation policies, and remediation processes. A chaotic response to a controlled experiment signals a need for improvement. This is why structured outage coordination is the critical other half of the reliability equation.

The Playbook: Integrating Chaos Experiments with Rootly

This practical guide shows SREs how to implement SRE chaos experiments integrated with Rootly, creating a powerful feedback loop for continuous improvement.

Step 1: Configure Your Chaos Experiment

First, set up your chaos experiment tool. While Chaos Monkey is the classic example, these principles apply equally to modern alternatives like Gremlin or LitmusChaos.

Start by configuring your tool to target a non-critical application or a staging environment. The key is to run controlled experiments that don't impact real users initially [8]. Ensure your experiment script or tool can make a simple API call when an experiment begins.

Step 2: Create a Rootly Webhook to Receive Signals

Rootly can ingest signals from any tool capable of sending a webhook. This is the entry point for your chaos experiment's alert.

To set this up, navigate to Rootly's integrations page and create a new Webhook integration. This action generates a unique URL that your chaos tool will call. Many tools use this simple but powerful method to connect with Rootly, from custom scripts to monitoring platforms like Checkly [4]. This flexibility is a core part of Rootly's design, allowing you to connect hundreds of tools into a single, cohesive workflow.

Step 3: Trigger a Rootly Incident Automatically

With the webhook in place, you can now connect your chaos tool to Rootly.

• Connect the Chaos Tool to Rootly: Modify your chaos experiment script to send a POST request to the Rootly webhook URL the moment a failure is injected. The payload of this webhook can contain valuable context about the experiment, such as the targeted service, the type of failure (for example, "VM termination"), and a unique experiment ID.
• Configure a Rootly Workflow: Next, set up a Workflow in Rootly that triggers when a new alert is received from your chaos tool's webhook source. This workflow automates the subsequent response, removing manual toil and ensuring every experiment is handled consistently.

Step 4: Automate the Entire Response Lifecycle

This is where the integration truly shines. The Rootly workflow you configured can automate the entire incident response lifecycle based on the incoming signal from your chaos experiment.

Here are some actions you can automate:

• Create an Incident: Automatically declare a new incident in Rootly and tag it with a "Chaos Experiment" label for easy filtering and analysis.
• Establish a Command Center: Instantly create a dedicated Slack or Microsoft Teams channel for the incident and invite the on-call SRE.
• Assign Roles: Automatically assign an Incident Commander to lead the response.
• Execute a Runbook: Attach a pre-defined runbook with diagnostic steps, guiding the responding engineer on what to investigate first.
• Pull in Context: Automatically fetch relevant dashboards from Datadog or logs from Splunk. Providing this context immediately helps the responder diagnose the issue faster, which is why integrations with observability tools are so powerful.

Expanding Your Toolkit: Gremlin, Litmus, and Other Integrations

The principles of integrating chaos experiments extend far beyond Chaos Monkey.

Beyond Chaos Monkey: Integrating with Modern Chaos Platforms

Addressing the need for Rootly chaos engineering integration gremlin litmus, it's important to note that the same integration pattern applies. Modern platforms like Gremlin and LitmusChaos offer more granular and targeted experiments, such as latency injection, packet loss, and resource exhaustion [6]. By using Rootly's generic webhook, you can trigger automated incident response workflows from any of these advanced tools, ensuring your team is prepared for a wide range of failure modes.

Leveraging a Rich Integration Ecosystem

Rootly’s power comes from its ability to connect your entire toolchain [2]. An incident triggered by a chaos experiment can also automatically create a Jira ticket for follow-up, ensuring that learnings are never lost. Furthermore, integrations with platforms like Cortex can enrich the incident with real-time service ownership data, ensuring the right people are notified instantly [1].

From Experiment to Improvement: Analyzing and Learning

The goal of a chaos experiment isn't just to survive it; it's to learn from it and improve. Rootly provides the tools to close this feedback loop.

Using Rootly for Post-Experiment Analysis

Rootly automatically logs every action—from the initial chaos alert to the final resolution step—in a chronological timeline. This timeline becomes the single source of truth for a blameless postmortem, eliminating guesswork and the tedious work of manual data gathering. This automated approach to documentation and analysis helps teams focus on insights, not administration.

Teams can review the timeline to identify bottlenecks in their response process. From there, they can create action items (for example, "Improve dashboard for Service X" or "Update runbook for Redis failure") directly in Rootly and sync them to project management tools like Jira.

Measuring the Impact on Reliability

By testing reliability with Chaos Monkey and Rootly, you can gather quantitative data on your team's performance. Track key metrics like Mean Time to Acknowledge (MTTA) and Mean Time to Resolve (MTTR) for incidents generated by chaos experiments. A steady decrease in these metrics over time provides clear proof that your systems and your team are becoming more resilient. You can track these and other important metrics with Rootly's incident analytics capabilities.

Conclusion: Build a Resilient System with Integrated Chaos and Response

Integrating chaos engineering tools like Chaos Monkey with an incident management platform like Rootly creates a closed-loop system: inject failure, automate the response, analyze the results, and improve. This approach moves reliability engineering from a reactive practice to a proactive, continuous improvement cycle.

This integration-first approach also paves the way for the future of automated incident management. With a well-defined API, incident response can be enhanced with AI agents that suggest remediation steps based on data from past chaos experiments [3].

Ready to build a more resilient system? Start small by running your first experiment against a single service in a staging environment.

Explore Rootly's extensive integration library and book a demo to see how you can turn chaos into a catalyst for improvement.

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