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Chaos101

What is Chaos Engineering?​

Modern applications are complex, distributed, and dynamic—often built with microservices and deployed on cloud-native infrastructure. With that complexity comes unpredictability. Chaos Engineering is the practice of proactively introducing faults to uncover weaknesses and ensure systems remain reliable under real-world conditions.

Chaos Engineering is the discipline of conducting controlled experiments to build confidence in a system’s ability to withstand turbulent conditions in production.

Why It Matters for Enterprises​

In the current state for modern applications, downtime isn't just inconvenient—it can damage reputation, revenue, and customer trust.

Harness Chaos Engineering (Harness CE) helps enterprise teams:

  • Minimize risks before incidents occur
  • Strengthen service reliability and SLAs
  • Validate failover mechanisms and autoscaling
  • Enable shift-left resilience testing during delivery

Chaos engineering acts as a resilience gate for production, uncovering systemic gaps in infrastructure, failover, observability, and SRE practices.

How It Works​

Chaos Engineering simulates failures such as:

  • Pod or node crashes
  • CPU/memory/network stress
  • Service/API latency or blackhole
  • Cloud infrastructure degradation (for example, EC2 termination, Azure disk loss)

Harness enables this through a structured workflow:

Chaos Engineering Overview

  1. Define steady state: What does healthy behavior look like?
  2. Form a hypothesis: What should happen during failure?
  3. Inject chaos: Simulate the fault with scoped blast radius.
  4. Observe and verify: Measure if the system maintained its SLOs.
  5. Remediate and improve: Use insights to harden services.

Built for the Enterprise​

Harness Chaos Engineering (Harness CE) goes beyond basic fault injection. It is purpose-built for scale and governance:

  • Governance and Security

    • Role-based access control (RBAC) and fine-grained permissions
    • Dedicated Workspaces and Chaos Teams for multi-tenancy
    • ChaosGuard: Prevent unsafe experiments from executing based on pre-defined conditions

  • Observability and Analysis

    • Integrates with APM tools (for example, Prometheus, Datadog, New Relic)
    • Health probes, hypothesis validation, and experiment scoring
    • Track resilience metrics and experiment impact over time

  • Integrated with CI/CD

    • Native integration with Harness CD Pipelines
    • Automate resilience testing as part of every deployment
    • Enforce resilience gates before production

  • Flexible Experimentation

    • Supports Kubernetes, AWS, Azure, GCP, and many more platform-specific chaos injection
    • Declarative experiment definition and Git-based chaos artifact sources (chaos-experiments-as-code)
    • Rich fault library with reusable and composable chaos scenarios

  • Scalable Architecture

    • Built on CNCF LitmusChaos with enhanced enterprise capabilities
    • Centralized control plane for managing all chaos infrastructure
    • Multi-cluster, multi-cloud support out of the box

Shift Left with Confidence​

The initial principles of chaos engineering recommend performing experiments in production, which is relevant and encouraged. This validates resilience beforehand, acting as a quality gate for larger deployment environments. The need to build confidence in a highly dynamic environment—where application services and infrastructure undergo frequent and independent upgrades—accelerates this process. The resulting paradigm includes:

  • Increased ad-hoc and exploratory chaos testing by application developers and QA teams;
  • Automating chaos experiments within continuous delivery (CD) pipelines.

With Harness CE, resilience is no longer an afterthought. It becomes a native part of the SDLC—from staging environments to production, across every application team.

Next Steps​

Ready to inject some resilience into your systems?

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