API monitoring wired to PagerDuty: change validation time fell 85%
At UScellular in 2025, I built the WDH API Validation and Monitoring tool, a Python and Flask app. It ran API health checks on demand or on a schedule, in place of checks engineers had run by hand.
Summary
- Role
- Lead Continual Service Improvement Engineer, UScellular, Jan 2021 – Aug 2025. Enterprise CSI Lead for the Wing Digital Hub and FlowOne task force with Nokia. I built this tool in 2025.
- Task force
- A five-track get-well program, with an operational owner for each track on the DevSecOps platform team.
- Results
- Change validation time fell 85%. Several major outages were caught before customers noticed.
- Tools
- Python and Flask · REST and GraphQL health checks · PagerDuty · Postman, for the hand-run checks the app replaced
Problem
- Engineers ran the API health checks by hand in Postman, several times a day.
- Each run took about 30 minutes.
What I did
- I built a Python and Flask app with a library of REST and GraphQL health checks.
- Checks ran on demand or on a schedule.
- A failed check opened a PagerDuty incident, and recovery closed it.
- The app handled retries and token refresh.
- It stored response times for trend and SLA reporting.
What changed
- Running the checks took a few clicks.
- Change validation time fell 85%.
- Several major outages were caught before customers noticed.
- Engineering leadership circulated the tool as an example of improving the network and the customer experience.
How a health check ran
- Scheduled or on-demand run
- REST or GraphQL health check
- PagerDuty incident: opened on failure, closed on recovery
- Stored response time
- Trend and SLA reporting
Related case study: Service Impact Detection, an AI network analysis tool that went into production in the Network Operations Center.
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