Quick answer
AI observability should show what happened to a business case across model calls, system actions, and human review. Track completed work, failures, queue age, corrections, latency, and cost. Connect those signals to an owner who can investigate and intervene; a dashboard alone is not an operating plan.
Follow the case, not just the model request
Give each business case an identifier that connects its input, processing steps, approvals, and destination result. An operator should be able to answer whether the case finished, is waiting, failed, or needs manual attention. A model request marked successful does not answer those questions.
OpenTelemetry describes traces, metrics, and logs as observability signals. In a business workflow, use them to connect system behavior with the process outcome. For example, a trace can help locate a slow integration, while a queue-age measure reveals whether that delay threatens the service commitment.
Choose measures that lead to action
Agree definitions with the process owner. A completed case should mean the intended business result was confirmed, not merely that a background task stopped running.
| Signal | Decision it supports |
|---|---|
| Completed cases and manual fallbacks | Is the system doing useful work? |
| Oldest waiting case | Does someone need to clear a queue? |
| Correction and rejection rates | Has quality or input mix changed? |
| Destination failures | Is an integration blocking completion? |
| Cost per completed case | Are retries or extra processing eroding value? |
| Unauthorized action attempts | Should action authority be paused and investigated? |
Keep useful evidence without logging everything
Record versions, event times, validation results, and permitted identifiers. Decide separately whether raw documents or prompts need to be retained and who may access them. Logging complete customer records by default can expand the data footprint without making diagnosis better.
A useful investigation record explains the input version, the proposed action, the approval, and the observed result. If sensitive evidence is necessary, keep it behind the appropriate controls and retention policy. Ensure the support team can diagnose failures through approved access rather than informal data exports.
Attach alerts to a response plan
For each alert, name the owner, urgency, and first action. A rising review queue might require additional operator capacity. A broken integration might require pausing writes and preserving cases for recovery. A quality regression might require returning to a prior release.
Test the response plan with a simulated failure before launch. Include a safe way to pause new actions and reconcile cases already in progress. Review alert volume after release: excessive low-value notifications make it harder for the team to notice a real incident.
- Who receives the alert during normal hours and outside them?
- What evidence do they need to investigate?
- Can they pause, retry, or escalate safely?
- How will they confirm recovery and communicate the impact?
Common questions
Is observability the same as evaluation?
No. Evaluation checks behavior against expected outcomes; observability helps explain live behavior. Production findings should inform evaluations, and evaluations should test changes before release.
What should management see?
A concise view of completed work, service delays, manual effort, and operating cost. Technical traces are useful for investigation but should not substitute for business outcomes.
Sources & further reading
Primary documentation for the technical points cited above. The decision frameworks and illustrative examples are PlatformBridge’s editorial guidance.