Help: Cribl is dropping events silently. How to debug a pipeline for loss?

Hey everyone, I've been working on a Cribl Stream deployment to route logs from a few Kubernetes clusters to an S3-based data lake. Overall it's been great, but I've hit a frustrating issue: I'm seeing a consistent 2-3% event loss between my source and destination, and there are no obvious errors in the Worker Process logs.

My pipeline is fairly standard:
* Source: A TCP JSON listener.
* Processing: A few Eval functions to add metadata, a Parser for some nested fields, and a Filter to drop specific debug events.
* Destination: An AWS S3 destination with gzip compression.

I've checked the basics:
* Worker Process metrics show no `events_dropped` or `routes_dropped`.
* The source's `events_in` matches my app's sent count, but `events_out` at the S3 destination is lower.
* My Filter function is simple, just dropping events where `level == "debug"`. I've double-checked the condition.

Has anyone else dealt with silent drops? I'm thinking the next step is to add a Passthru destination temporarily to see if events make it through the pipeline, but I'd love to hear your debugging playbooks.

My main questions are:
1. Where are the less obvious places to look for loss? Could it be in the Destination's batching or compression?
2. Are there specific pipeline functions known to cause silent issues with certain data shapes?
3. What's the most effective way to add internal logging/tap points in a pipeline without disrupting flow?

Here's a snippet of my Filter function's condition, in case I'm missing something:

```javascript
// Filter to drop verbose debug logs
if (__event.level === 'debug') {
return null;
}
// All other events flow through
return __event;
```

Any pointers would be much appreciated. I'll share my findings once I get to the bottom of it.

-- Amy