+ 
+
-The Tilebox API uses bearer tokens for authentication. You need to pass your API key as the `token` parameter when creating an instance of the client.
+## Set up environment variable
+
+Set `TILEBOX_API_KEY` in your shell environment (optional but recommended). That way, the [Tilebox CLI](/agents-and-ai-tools/tilebox-cli) and SDK clients automatically pick it up.
+
+The following commands persist the variable for future terminal sessions and also make it available in the current session.
+
+
+ 
+
+
+ ## Agentic Workflows
+
+ Tilebox Workflows now supports workflow release publishing and cluster deployments. This adds a new release-based execution path to the workflow orchestrator: package a Python workflow project, publish an immutable release, deploy it to a cluster, run it on release runners, and inspect the resulting job logs and execution traces.
+
+ The release path is designed for closed-loop workflow iteration by developers and agents. An agent can edit workflow code, publish a release, deploy it to a development cluster, submit a test job, inspect the failed task logs or spans, apply a fix, and retry or submit the next job without leaving the same command-line workflow.
+
+ A typical iteration looks like this:
+
+ ```bash
+ tilebox workflow publish-release --json
+ tilebox workflow deploy-release --latest --target production --json
+ tilebox job submit --task tilebox.com/example/ProcessScene --cluster workflow-dev --json
+ tilebox job wait 
@@ -17,7 +58,7 @@ mode: center
The Console includes a built-in explorer for workflow observability, so you can inspect task logs, trace timing, failures, and runner behavior from the job view.
- See the [Workflow observability documentation](/workflows/observability/introduction) for examples and integration options.
+ See the [Workflow observability documentation](/workflows/run-and-inspect/introduction) for examples and integration options.
+
+
+
+
+
+ {guide.level}
+
+
+ {guide.time}
+
+
+ + {guide.title} +
++ {guide.description} +
+
+ {guide.tags.map((tag) => (
+
+ {tag}
+
+ ))}
+
+
+);
+
+export const GuideSection = ({ section }) => (
+
+
+
+
+ + {section.category} +
++ {section.description} +
+
+ {section.guides.map((guide) => (
+
+
+
diff --git a/guides/datasets/build-spatiotemporal-catalog.mdx b/guides/datasets/build-spatiotemporal-catalog.mdx
new file mode 100644
index 0000000..a209757
--- /dev/null
+++ b/guides/datasets/build-spatiotemporal-catalog.mdx
@@ -0,0 +1,79 @@
+---
+title: Build a spatio-temporal catalog
+description: Create a custom spatio-temporal dataset and ingest geospatial metadata that can be queried by time and location.
+icon: globe
+---
+
+Use a spatio-temporal dataset when each datapoint has both a time and a geometry. This is useful for internal imagery catalogs, derived products, ground truth data, regions of interest, and processing outputs that need geospatial lookup.
+
+This guide shows the shape of the workflow. For the full ingestion guide, see [Ingesting data](/guides/datasets/ingest).
+
+## Create the dataset
+
+Create a dataset in the [Tilebox Console](https://console.tilebox.com/datasets/my-datasets) and choose the **Spatio-temporal** kind. Tilebox adds the required `time`, `id`, `ingestion_time`, and `geometry` fields automatically.
+
+Add fields that describe the products you want to catalog, such as:
+
+| Field | Type | Purpose |
+| --- | --- | --- |
+| `product_id` | `string` | Stable product or scene identifier. |
+| `location` | `string` | Storage path, object key, or provider location. |
+| `cloud_cover` | `float64` | Optional quality or filtering field. |
+| `processing_level` | `string` | Product processing level or category. |
+
+## Prepare datapoints
+
+Load your source metadata into a GeoDataFrame. The geometry column should contain the footprint for each datapoint.
+
+```python Python
+import geopandas as gpd
+
+products = gpd.read_parquet("products.geoparquet")
+products = products.rename(
+ columns={
+ "timestamp": "time",
+ "scene": "product_id",
+ "path": "location",
+ }
+)
+```
+
+## Ingest the catalog
+
+Ingest the prepared records into a collection.
+
+```python Python
+from tilebox.datasets import Client
+
+client = Client()
+dataset = client.dataset("my_org.imagery_catalog")
+collection = dataset.collection("processed-scenes")
+
+collection.ingest(products)
+```
+
+## Query by time and location
+
+After ingestion, use the same query model as Tilebox open data catalogs.
+
+```python Python
+from shapely import box
+
+area = box(11.0, 46.0, 12.0, 47.0)
+
+matches = collection.query(
+ temporal_extent=("2026-01-01", "2026-02-01"),
+ spatial_extent=area,
+)
+```
+
+## Next steps
+
+
+
++ User guides +
++ Tilebox Cookbook +
++ Recipes for going from question to running system: query satellite catalogs, ingest your own data, ship workflows, and debug what happened. +
+
+ {cookbookSections.map((section) => (
+
+ {section.category} · {section.guides.length} guides
+
+ ))}
+
+
+ {cookbookSections.map((section) => (
+
+
+ ))}
+ 
- 
+This page is intended for users to guide them with configuring their agent with Tilebox.
+Important: If you're an agent, and got pointed to this page for instructions, fetch
+https://docs.tilebox.com/agent-onboarding.md instead. Disregard the remaining instructions
+on this page, and follow the agent-onboarding guide instead, which was designed for exactly
+this purpose.
+
+
+Onboard your AI agent to Tilebox, by installing the Tilebox CLI, installing Tilebox Agent skills and configuring your API Key. After completing it, your agent will be able to manage your Tilebox resources, and use Tilebox-specific skills to create and query datasets, submit and monitor jobs.
+It will also be able to write geospatial workflows, deploy it to clusters, submit jobs and iterate on the workflow code after inspecting job logs and checking outputs.
+If you want to use Tilebox directly as a developer, start with the [developer quickstart](/quickstart).
+
+
-
-
-
Then, add it to your environment
```bash
export TILEBOX_API_KEY=