Automation Rescue: Fixing Flaky Lambdas
A recent incident where background automations started failing intermittently. This case study walks through how I traced the behavior into Lambda-style functions, fixed the root causes, and left the pipeline more observable and reliable than before.
Context
The application relied on serverless functions (Lambda-style handlers) to perform automation tasks — things like sending notifications, processing payloads from webhooks, and updating downstream systems after a deployment. Under normal load everything looked fine, but during real-world traffic the automations became flaky.
Symptoms included stuck executions, retries, and functions that would pass locally but fail once deployed behind the platform's Lambda/runtime layer.
Symptoms & investigation
- Intermittent failures: requests would sometimes succeed and sometimes time out with no clear pattern.
- Limited visibility: default logs were not enough to see memory usage, cold starts, or where the handler was spending time.
- Different behavior locally vs. in the cloud: the same code was stable in local dev but unstable in the deployed Lambda-style environment.
I started by adding structured logging around the function boundaries: entry/exit logs, correlation IDs, and timing metrics. From there, I could see patterns in duration, memory usage, and retry behavior across executions.
What I changed
- Tuned resource limits: adjusted memory and timeout settings so the functions had enough headroom under real workloads, not just test cases.
- Idempotent handlers: hardened the automation logic so retries would not double-process the same event and cause noisy side effects.
- Retry & DLQ strategy: set clear rules for when to retry, when to fail fast, and where to send poison messages so they wouldn't block the rest of the queue.
- CI/CD checks: added lightweight smoke tests that hit the deployed function endpoint after a release to confirm that the path, IAM permissions, and environment variables were wired correctly.
Impact
- Automation success rate increased and stayed stable across different traffic patterns.
- Incidents became easier to explain: logs and metrics show exactly what each execution did and why it failed when it does.
- Future Lambda-style functions can reuse the same patterns for logging, timeouts, and retries instead of starting from scratch.
This project is a good example of how I approach problems: instrument first, then tune, then standardize the pattern so future work benefits from the incident.