As distributed systems scale across regions, clouds, and deployment models, manual synthetic monitoring quickly becomes unmanageable. Ad hoc checks configured through user interfaces cannot keep pace with ephemeral infrastructure, continuous delivery pipelines, and dynamic service meshes. In AIOps-driven environments, where machine learning models depend on clean, consistent telemetry, unmanaged synthetic checks introduce noise rather than clarity.
Synthetic Monitoring as Code (SMaC) addresses this gap by treating reliability tests like any other software artifact: version-controlled, peer-reviewed, and deployed through automation pipelines. For DevOps and SRE teams, this approach creates repeatability. For AIOps systems, it provides structured, predictable signals that can be correlated with metrics, logs, and traces.
This tutorial walks through a hands-on lab for managing synthetic checks using Terraform and a modern observability platform. By the end, you will have a scalable framework for creating, versioning, and integrating synthetic tests directly into your AIOps workflows.
Why Synthetic Monitoring as Code Matters in AIOps
Synthetic monitoring simulates user interactions or API calls to validate availability and performance. Traditionally, these checks are configured manually in dashboards. While convenient initially, this model breaks down as environments expand. Configuration drift becomes common, and there is often no reliable audit trail of changes.
In AIOps contexts, consistency is critical. Machine learning models that detect anomalies rely on predictable inputs. If synthetic tests are frequently altered without version control, the resulting signal shifts can degrade model accuracy. Research suggests that structured observability pipelines improve the quality of downstream automation and correlation engines.
Monitoring as code introduces several advantages:
- Version control: Synthetic checks are stored alongside application code.
- Peer review: Changes undergo pull request validation.
- Environment parity: The same test definitions deploy across staging and production.
- Automated lifecycle management: Checks are created and destroyed with infrastructure.
For AIOps teams, this structure means synthetic signals become reliable inputs for event correlation, root cause analysis, and automated remediation workflows.
Lab Setup: Defining Synthetic Checks with Terraform
Terraform enables declarative infrastructure management across many observability platforms. While specific resource names vary by provider, the pattern remains consistent: define a synthetic test as a code block, parameterize it, and apply it through a pipeline.
Step 1: Structure Your Repository
Create a repository structure that mirrors your environment topology:
modules/synthetic-api/modules/synthetic-browser/environments/staging/environments/production/
Modules encapsulate reusable test definitions. Environment folders supply variables such as endpoints, regions, and alert thresholds.
Step 2: Define an API Synthetic Check
A simplified Terraform configuration might look conceptually like this:
resource "observability_synthetic_test" "api_health" {
name = "orders-api-health"
type = "api"
request_url = var.api_endpoint
method = "GET"
assertions {
type = "statusCode"
operator = "equals"
target = 200
}
locations = var.test_locations
frequency = var.check_interval
}This declarative definition ensures the API health check is reproducible. Any changes to frequency, locations, or assertions are captured in version history.
Step 3: Parameterize for Scale
Rather than duplicating configurations, use variables and maps to generate multiple tests dynamically. For example, define a list of microservices and iterate over them to create uniform health checks. This pattern is particularly powerful in microservices architectures where services are added frequently.
Many practitioners find that standardized modules reduce configuration errors and improve signal consistency for downstream AIOps analysis.
Integrating Synthetic Checks into AIOps Pipelines
Defining synthetic tests as code is only the first step. The real value emerges when these tests are embedded in CI/CD and AIOps workflows.
Pipeline Integration
Include Terraform validation and planning stages in your CI pipeline. A typical flow:
- Developer submits pull request modifying a synthetic test.
- CI runs
terraform validateandterraform plan. - Team reviews expected changes.
- Upon approval, pipeline applies updates.
This process ensures synthetic checks evolve alongside application changes. If a new endpoint is introduced, its synthetic validation is deployed simultaneously.
Event Correlation in AIOps
When synthetic failures occur, they generate structured events. In an AIOps platform, these events can be correlated with infrastructure metrics, logs, and deployment markers. Evidence indicates that combining synthetic and real-user telemetry improves contextual awareness during incident response.
For example, if a synthetic browser test fails immediately after a deployment event, the AIOps engine can prioritize that deployment as a likely causal factor. Automated runbooks may then trigger rollback workflows or notify responsible teams.
Feedback Loops and Continuous Improvement
Because synthetic definitions are code, improvements can be iterative. Teams can refine assertions, add latency thresholds, or expand geographic coverage based on incident retrospectives. These refinements become part of the reliability knowledge base, strengthening future anomaly detection.
Best Practices for Scalable Synthetic Monitoring
As with any automation strategy, governance and design discipline matter.
Design for Signal Quality
AIOps models depend on meaningful signals. Avoid creating redundant synthetic tests that produce duplicate alerts. Instead, align each test with a specific user journey or service-level objective. Clear intent reduces noise and enhances machine-driven correlation.
Tag and Classify Everything
Apply consistent tags such as service, environment, team, and criticality. Structured metadata enables AIOps platforms to group related events and identify systemic issues rather than isolated symptoms.
Manage Lifecycle Automatically
Ephemeral environments should not leave behind orphaned synthetic checks. Use Terraform destroy workflows tied to environment teardown processes. This prevents outdated checks from skewing anomaly baselines.
Validate Before Production
Test synthetic definitions in staging before promoting them. Misconfigured assertions can create false positives, which in turn degrade trust in AIOps automation. Many teams adopt progressive rollouts for new monitoring configurations, similar to application feature releases.
Common Pitfalls and How to Avoid Them
Over-monitoring: Creating excessive checks can overwhelm both humans and algorithms. Focus on business-critical paths.
Static thresholds: Hardcoded thresholds may not reflect evolving baselines. Where supported, combine static assertions with adaptive anomaly detection.
Siloed ownership: Synthetic monitoring should not belong solely to operations. Embed responsibility within product teams to ensure checks reflect real user expectations.
Ignoring cost implications: Synthetic execution frequency and geographic coverage can impact spend. Align cadence with risk tolerance and service importance.
Conclusion: From Scripts to Strategic Signals
Synthetic Monitoring as Code transforms reliability validation from a manual configuration task into a strategic engineering discipline. By defining tests declaratively, versioning them, and integrating them into CI/CD pipelines, DevOps teams create durable, scalable observability foundations.
For AIOps practitioners, the benefits extend further. Structured, consistent synthetic signals enhance event correlation, anomaly detection, and automated remediation. Rather than reacting to fragmented alerts, teams gain context-rich insights driven by both simulated and real user telemetry.
As environments continue to grow in complexity, the convergence of Infrastructure as Code, observability automation, and AIOps will likely become standard practice. Synthetic Monitoring as Code is not merely a convenience—it is a prerequisite for intelligent, automated operations at scale.
Written with AI research assistance, reviewed by our editorial team.
