Runtime Logs and Monitoring
Runtime Logs and Monitoring help teams understand what is happening after code starts running.
What this solves
This guide helps developers use runtime logs and monitoring as part of an AI backend delivery workflow instead of treating generated code as an unverified output.
When to use it
Use it when a generated backend needs to be inspected, executed, repaired, documented, deployed, or monitored by the team responsible for the software.
Step-by-step workflow
Open logs after local execution or deployment, review API requests and service state, identify failures, and feed concrete runtime evidence back into fixes.
Expected result
Developers and tech leads can see whether the AI-generated backend is healthy, observable, and ready for continued iteration.
Common issues
Most issues come from incomplete requirements, missing runtime configuration, unclear data models, or API behavior that has not been validated with logs and documentation.