Monitoring by default: the minimum viable signals

Monitoring for AI systems is usually framed as “track everything.” In practice, the first monitoring system that works is the one that survives a busy on-call rotation.

Here’s a pragmatic approach: define a minimum viable signal set—a few signals that catch the most common, highest-impact failures.

The minimum viable signal set

Start with signals that are:

• Cheap to compute
• Hard to game
• Stable across model and prompt refactors
• Actionable (someone can do something when it fires)

In most production AI systems, the minimum set includes:

1) Input health (data quality)

• Missing or malformed fields
• Language/locale shifts
• Unexpected length or format distributions

These catch upstream breakages that look like “the model got worse” but aren’t.

2) Policy + safety events

• Content filter triggers / blocked outputs
• PII detections
• “Refusal” or “can’t comply” rates (when relevant)

These are often your earliest warning that the system is being used out of scope.

3) Output quality proxies

You can’t label everything, so use consistent proxies:

• Citation / grounding rate (if applicable)
• Unsupported-claim detector rate (even a crude one helps)
• User correction / retry rate

The key is to pick proxies you can keep stable for months.

4) Latency + availability

AI failures are frequently just systems failures:

• p50 / p95 latency
• timeout rates
• dependency error rates

If the system is slow or flaky, users will route around it—and your quality metrics will lie.

5) Drift + regression canaries

Even if you don’t run heavy drift detection, you can:

• Keep a small canary suite (fixed prompts / fixtures)
• Run it on every release and on a schedule
• Alert on regressions beyond a set threshold

Tie signals to a monitoring plan

Signals are only half the work. A monitoring plan should answer:

1. What does “bad” look like?
2. Who gets paged?
3. What’s the first response playbook?
4. When do we roll back?

This is why monitoring belongs in an Assurance Pack: it connects production reality back to intended use and evaluation evidence.

Avoid the common trap

The common failure mode is building dashboards without decisions.

If a metric can’t trigger one of these, it’s not part of the minimum set (yet):

• Roll back a release
• Disable a feature
• Tighten a policy boundary
• Add a mitigation
• Expand evaluation coverage

Start small. Ship the minimum signal set. Then grow coverage based on actual incidents—not imagined ones.