> ## Documentation Index
> Fetch the complete documentation index at: https://docs.transluce.org/llms.txt
> Use this file to discover all available pages before exploring further.

# Agentic Ingestion

> How the Docent Agent generates ingestion scripts

This page explains how the Docent Agent handles ingestion under the hood.

To ingest your data, point the Docent Agent to a directory containing your trajectories. For best results, sort your trajectories by format before invoking the agent.

```text wrap theme={null}
/docent Ingest the trajectories at <PATH_TO_DATA>
```

## How the Docent Agent handles ingestion

The Docent Agent uploads your agent logs into Docent by writing a Python script that converts them into `AgentRun` format. It investigates your file structure, examines your trajectory format, and maps each field in your schema to a Docent object. It produces:

1. `ingestion-plan.md`: A mapping of your trajectory fields to Docent's data model, including any fields that will be intentionally omitted. Review this file to verify the agent intends to organize and display your data in a suitable format.
2. `ingest.py`: A Python script that reads your logs and uploads them via the SDK. You can modify and rerun this as needed.

The Docent Agent asks clarifying questions if your data format is ambiguous or it needs more context about how you want the data structured.

If the agent can't infer your format or you need fine-grained control, write the ingest script directly. See [SDK ingestion](/ingestion/sdk).

## What's next

* **Ready to analyze?** See the [Analysis overview](/analysis/overview).
* **Want to tweak the ingestion script yourself?** See [SDK ingestion](/ingestion/sdk) for the underlying API.