What Is AI Observability? Tracing Agents in Prod
Traditional monitoring tells you a server is up. It cannot tell you an agent gave a wrong answer politely and quickly. AI observability fills that gap.
- Published
Traces: the core
A trace records everything that happened for one task: the prompt, each model call, every tool call with inputs and outputs, retrieved documents, the final answer, tokens and timings. When something goes wrong, the trace shows exactly where.
What to track
| Metric | Why it matters |
|---|---|
| Task success rate | The number that matters to the business |
| Escalation / hand-off rate | Rising rates signal quality drift |
| Cost per task | Catches prompt bloat and loops |
| Latency (p50, p95) | Slow agents get abandoned |
| Tool error rate | Integrations break silently |
| User feedback | Thumbs, ratings, complaints |
Quality in production
Sample real traces daily and grade them — automatically with an LLM grader calibrated against human judgement, plus a small human-reviewed sample. Failures become new eval cases, so the same mistake is caught before the next release.
Privacy
Traces contain customer data. Redact sensitive fields, restrict access, and set retention periods — observability should not become your biggest data-protection risk.
Frequently asked questions
Do we need a special tool?
Dedicated AI tracing tools help, but OpenTelemetry-based tracing into your existing stack works well too.
How much data should we keep?
Full traces for a short window, aggregated metrics for longer, and redacted samples for evals.
When should observability be set up?
Before launch. Retrofitting it after an incident is slower and you lose the evidence.
