Who should use the Perform root cause analysis workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Development
Practical execution plan for perform root cause analysis with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
Organizational learning captured and systemic improvements implemented
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
Organizational learning captured and systemic improvements implemented
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Datadog to a consolidated incident timeline and data set ready for analysis. Then, you pass the output to BigPanda to a prioritized list of 3-5 hypotheses to test. Then, you pass the output to Parasoft Continuous Quality Testing Platform to confirmed root cause with evidence from one or more experiments. Then, you pass the output to Digma SRE AI Platform to a documented action plan with corrective and preventive steps. Then, you pass the output to InfluxDB to root cause resolved and verified in production. Finally, incident.io is used to organizational learning captured and systemic improvements implemented.
Define problem scope and collect incident data
A consolidated incident timeline and data set ready for analysis
Generate and prioritize hypotheses
A prioritized list of 3-5 hypotheses to test
Test hypotheses with targeted experiments
Confirmed root cause with evidence from one or more experiments
Identify corrective and preventive actions
A documented action plan with corrective and preventive steps
Implement and verify the fix
Root cause resolved and verified in production
Document findings and update runbooks
Organizational learning captured and systemic improvements implemented
Start by clearly documenting the observed symptom (e.g., performance degradation, error spike) and gather all relevant logs, metrics, traces, and user reports. Ensure you have a precise timestamp range and affected system components to narrow the investigation.
Why Datadog: Datadog combines infrastructure monitoring, APM, and log aggregation in one platform, directly matching all three needs for this step.
Brainstorm possible root causes based on the collected data and system architecture. Use techniques like the '5 Whys' or fishbone diagram to create a list of hypotheses, then prioritize them by likelihood and impact using historical patterns and expert judgment.
Why BigPanda: Tellius offers automated data insights and conversational querying, which can help generate and prioritize hypotheses from incident data.
For each high-priority hypothesis, design a minimal experiment to confirm or refute it. This may involve A/B testing a fix in a staging environment, querying specific log patterns, or temporarily rolling back a recent deployment. Execute experiments one at a time to avoid confounding results.
Why Parasoft Continuous Quality Testing Platform: Parasoft Continuous Quality Testing Platform supports automated testing in staging environments, directly enabling hypothesis testing.
Once the root cause is confirmed, define immediate corrective actions to mitigate the issue (e.g., hotfix, rollback) and longer-term preventive measures (e.g., monitoring alerts, code review checklists, automated tests). Document the actions with owners and deadlines.
Why Digma SRE AI Platform: Digma SRE AI Platform provides remediation suggestions alongside root cause analysis, directly supporting corrective and preventive action identification.
Deploy the corrective action to production following your change management process (e.g., canary release, blue-green deployment). Monitor the same metrics that indicated the problem to confirm the fix resolves the symptom without introducing new issues.
Why InfluxDB: InfluxDB provides real-time anomaly detection and data visualization, enabling verification of the fix through monitoring dashboards.
Write a post-incident report summarizing the symptom, root cause, experiments, and actions taken. Update runbooks and monitoring alerts to detect or prevent similar issues in the future. Share the report with the team and schedule a blameless postmortem meeting.
Why incident.io: incident.io provides incident response and status page updates, directly supporting documentation and runbook updates.
§ Before you start
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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