Observability tells you what is happening inside a running system.
Production reliability asks a different question:
What does this change mean for production?
The two are closely related, but they operate at different points in the engineering workflow.
What observability gives you
Observability helps engineers understand system behavior through signals such as:
- Logs
- Metrics
- Traces
- Errors
- Latency
- Resource usage
- Request behavior
For AI systems, that can extend to prompts, responses, tool calls, retrieved context, token usage, and evaluation results.
Observability is extremely useful when you need to understand what is happening.
What observability does not tell you before a change ships
Imagine a pull request changes a shared database library.
Your observability system can tell you what happens after deployment.
But before deployment, you also want to know:
- Which services use the library?
- How important are those services?
- What tests cover the change?
- Has the component caused incidents before?
- How large is the potential blast radius?
- Can the deployment be rolled back?
Those questions require change context.
Production reliability uses observability as evidence
A useful model is:
Code change
+
Production context
+
Observability signals
↓
Reliability assessment
Observability is therefore an input.
It does not replace the broader assessment.
Example
Suppose a change modifies a payment service.
Observability might show that:
- Error rates are stable
- Latency is normal
- No dependency is currently failing
That is useful.
But you may still need to know whether the change touches a high volume path, whether the relevant integration tests exist, and whether similar changes caused incidents before.
Healthy runtime behavior does not automatically mean every new change is reliable.
AI systems make this distinction clearer
An AI application can have healthy infrastructure while producing bad results.
AI observability can help you inspect:
- Prompt and response behavior
- Tool calls
- Retrieved context
- Latency
- Cost
- Evaluation results
Production reliability asks what a change to that system could mean before it reaches users.
The main difference
Observability: What is happening?
Production reliability: What does this change mean for production?
Both are useful.
Neither needs to replace the other.
Where PRI fits
The Production Reliability Index (PRI) combines different signals around a software change, including runtime information, code volatility, dependencies, deployment context, and other reliability indicators.
The purpose is to make change level reliability easier to assess before deployment.
Run a PRI assessment:
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