Shared agents
Span, trace or sessionLogs
All indexed, no sampling.
- Observability & Monitoring
//AI Observability
Monitor, evaluate,and improve your AI.
Most tools log what your AI did. AI observability also tells you whether it was any good, and helps you make it better.
Trace
See what happened
Every prompt, tool call and agent handoff captured as a trace, with token cost and latency on each span.
Evaluate
Know if it was good
Outputs scored for relevance, hallucination, toxicity and bias, on live traffic and before you ship.
Improve
Make it better
Compare experiments, review with humans, and promote the version that actually improved.
A hallucination returns HTTP 200.
Monitoring tells you it ran. Evaluation tells you it was good.
//Overview
What is AI observability?
AI observability is the practice of monitoring, evaluating and improving AI systems in production: capturing telemetry from LLM and agent calls, scoring the quality of their outputs and feeding the results into the next version.
DataObserve Sense scores quality on live traffic with online evals, runs offline experiments on versioned datasets, offers built-in or custom judges and puts a human in the loop for review, all on the same telemetry as your infrastructure.
//One loop
From a live score to thenext version you ship.
Live traffic
Score quality on live traffic
Production traces scored as they arrive, with LLM-as-a-judge on your own keys or your own scoring service.
- Span, trace or session
- Thresholds
- Scores on the span
//Explore more withDataObserve
More from Sense · Observability & Monitoring
Metrics
PromQL and SQL, full resolution, retained.
- Observability & Monitoring
Traces
OpenTelemetry-native, no sampling.
- Observability & Monitoring
Frontend Monitoring
Real user monitoring, session to stack trace.
- Observability & Monitoring
//FAQs
AI Observability: questions teams ask
What is AI observability?
Monitoring, evaluating and improving AI in production: telemetry from LLM and agent apps, paired with quality scores.
How is it different from LLM observability?
LLM observability is the monitoring layer. AI observability adds evaluation and improvement on top.
Do I have to replace my tools?
No. Anything exporting OpenTelemetry lands here, and other tools' scores can arrive as span attributes.
What is an LLM judge?
An LLM judge is a language model that grades another model's output against a rubric, such as accuracy, relevance or safety, so quality can be scored at scale.
Why isn't monitoring enough for AI?
Monitoring tells you an AI call ran and how long it took. Evaluation tells you whether the answer was any good, which is what users care about.
