---
Title: "What Is AI Observability? Monitoring AI Agents"
Url: "https://computer.io/glossary/ai-observability"
Topic: "AI safety"
Description: "AI observability is monitoring what AI systems do, including inputs, outputs, tool calls, costs and quality, so teams can debug and improve them."
---

# What is AI observability?

AI observability is the practice of monitoring and understanding what AI systems do in production, tracing inputs, retrieved context, outputs, tool calls, latency, costs and quality, so teams can debug failures, catch regressions and improve performance.

## What to track

| Signal | Why |
| --- | --- |
| Traces | See each step and tool call |
| Quality scores | Accuracy and helpfulness |
| Escalations | Where the agent gives up |
| Latency | User experience |
| Cost | Tokens and credits |
| Feedback | User ratings |

## Observability vs evals

Evals test before release; observability monitors live behavior. Both feed each other.

## FAQ

### Why is AI observability important?

AI behaves non-deterministically, so you need visibility into each step.

### What is LLM tracing?

Recording each model call, retrieval and tool call in a request.

### Is AI observability the same as logging?

It builds on logging with AI-specific traces and quality metrics.

## Related terms

- [AI evals](https://computer.io/glossary/ai-evals)
- [Agent orchestration](https://computer.io/glossary/agent-orchestration)

## How Computer helps

- [Agent studio](https://devrev.ai/agent-studio)

## Further reading

- [Build enterprise ai agents](https://devrev.ai/blog/build-enterprise-ai-agents)