# Getting started
<a name="datasets-getting-started"></a>

This topic provides an end-to-end workflow for creating, populating, and publishing a dataset.

## Dataset schemas
<a name="datasets-gs-schema-overview"></a>

Each dataset declares a `schemaType` at creation time. AgentCore validates every example against the declared schema before accepting it. Two schema types are supported:
+  **AGENTCORE\_EVALUATION\_PREDEFINED\_V1** — For testing agents against pre-written conversation turns. Required fields: `scenario_id`, `turns` (non-empty list; each turn must contain `input`).
+  **AGENTCORE\_EVALUATION\_SIMULATED\_V1** — For synthetic conversation generation. Required fields: `scenario_id`, `actor_profile` (object with required `context` and `goal`), `input`.

For full schema field definitions, examples, and ground truth mapping, see [Dataset schema](dataset-evaluations-schema.md).

## End-to-end workflow
<a name="datasets-gs-workflow"></a>

The following example demonstrates the complete dataset lifecycle: create, add examples, list examples, publish a version, and clean up.

**Example**  

1. 

   ```
   # 1. Create dataset
   agentcore add dataset --name my_eval_dataset \
       --schema-type AGENTCORE_EVALUATION_PREDEFINED_V1
   
   # 2. Add your scenarios to the JSONL file
   #    File: agentcore/datasets/my_eval_dataset.jsonl
   
   # 3. Deploy to create the dataset and sync examples
   agentcore deploy
   
   # 4. Publish version 1
   agentcore dataset publish-version --name my_eval_dataset
   
   # 5. Check status (shows versions and example count)
   agentcore status --type dataset
   
   # 6. Download a published version to local file
   agentcore dataset download --name my_eval_dataset --version 1
   
   # 7. Cleanup
   agentcore remove dataset --name my_eval_dataset
   agentcore deploy
   ```

1. 

   ```
   from bedrock_agentcore.evaluation import DatasetClient
   
   client = DatasetClient(region_name="us-west-2")
   
   # 1. Create dataset (polls until ACTIVE)
   ds = client.create_dataset_and_wait(
       datasetName="my_eval_dataset",
       schemaType="AGENTCORE_EVALUATION_PREDEFINED_V1",
       source={
           "inlineExamples": {
               "examples": [
                   {
                       "scenario_id": "TC-01",
                       "turns": [{"input": "What is my balance?", "expected_response": "Your balance is $50."}],
                       "assertions": ["Response includes a dollar amount"],
                   }
               ]
           }
       },
   )
   dataset_id = ds["datasetId"]
   print(f"Created: {dataset_id}, status={ds['status']}")
   
   # 2. Add more examples
   ds = client.add_examples_and_wait(
       datasetId=dataset_id,
       source={
           "inlineExamples": {
               "examples": [
                   {"scenario_id": "TC-02", "turns": [{"input": "Transfer $100", "expected_response": "Transfer complete."}]}
               ]
           }
       },
   )
   print(f"Example count: {ds['exampleCount']}")
   
   # 3. List examples
   resp = client.list_dataset_examples(datasetId=dataset_id)
   for example in resp["examples"]:
       print(f"  {example['exampleId']}: {example['scenario_id']}")
   
   # 4. Publish version 1
   ds = client.create_dataset_version_and_wait(datasetId=dataset_id)
   print(f"Published, draftStatus: {ds.get('draftStatus')}")
   
   # 5. List versions
   resp = client.list_dataset_versions(datasetId=dataset_id)
   for v in resp["versions"]:
       print(f"  Version {v['datasetVersion']}: {v['exampleCount']} examples")
   
   # 6. Cleanup
   client.delete_dataset_and_wait(datasetId=dataset_id)
   ```