> For the complete documentation index, see [llms.txt](https://docs.interactive.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.interactive.ai/governance/logs.md).
# Logs
Logs is the **raw output** of your deployed containers: agents, services, and databases. Where Observability records what an agent decided and Analytics records how its container performed, Logs is what the process actually printed.
Pick a workload and a time range and the stream updates on whatever refresh interval you set. History reaches back 72 hours.
### Reading a line
Each line carries a timestamp, a level, and a message. The level filter counts what it finds as well as filtering, so you can see there are thirty-seven errors in the window before deciding whether to open any of them.
Opening a line shows the structured fields behind it. Every line carries a core set: trace id, level, timestamp, message, the logger that emitted it, and the function and line number it came from. Beyond that a line carries whatever the code attached, so a routine evaluation arrives with its routine, node count, and duration as separate fields rather than buried in the message text. The JSON Fields column counts them, and the whole payload is available raw as JSON.
{% hint style="info" %}
Logs is container output. To see what an agent did inside a request, see [Observability](/governance/observability.md). For traffic, latency, and resource use, see [Analytics](/governance/analytics.md).
{% endhint %}
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.
Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:
```
GET https://docs.interactive.ai/governance/logs.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
