# AI observability

URL: https://www.getdynamiq.ai/glossary/ai-observability

> The practice of recording every step an agent or workflow takes so its behavior, cost and failures can be inspected after the fact.

The practice of recording every step an agent or workflow takes so its behavior, cost and failures can be inspected after the fact.

AI observability captures what happened during a run, not only what it produced. That includes which model answered, what it was asked, which tools it called and with what arguments, what came back, how many tokens were used, and how long each step took. For an agent that reasons across several steps, the final answer alone does not explain why it arrived there.

A wrong or contested answer in a regulated workflow needs to be explainable after the fact. Being able to reconstruct exactly which document an agent retrieved, or which tool it called, is what turns a dispute into a short investigation instead of an unanswerable question.

A support lead investigating an incorrect refund opens the run and finds the exact policy passage the agent retrieved and the tool call that issued the refund, rather than guessing at what happened.

**In Dynamiq**, every run of a deployed App is recorded with monitoring charts for cost, tokens, requests and latency, and a full node-by-node trace browsable as a graph, a tree or a timeline, or downloadable as JSON. An agent that reviews production traces for issues and regressions runs alongside this, surfacing failures before a person has to go looking for them.

## See it in Dynamiq

-   [Observability](https://www.getdynamiq.ai/product/observability)

## Related terms

-   [LLM tracing](https://www.getdynamiq.ai/glossary/llm-tracing)
-   [LLM evaluation](https://www.getdynamiq.ai/glossary/llm-evaluation)
-   [Online evaluation](https://www.getdynamiq.ai/glossary/online-evaluation)

## See an agent on your own workflow.

Bring a process and its documents. Our engineers will show you how Dynamiq runs it, in your environment or ours.

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