REST API

curl -X POST "{base_url}/api/v1/agentExecutions" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json" \
  -d '{
    "agent": {
      "id": "7f47e5f6-92bc-4a61-9cab-6bcc34b36c51",
      "type": "aiApplication",
      "name": "customer_support_agent",
      "fullyQualifiedName": "customer_support_agent"
    },
    "agentId": "7f47e5f6-92bc-4a61-9cab-6bcc34b36c51",
    "timestamp": 1787702400000,
    "endTimestamp": 1787702402400,
    "status": "Success",
    "input": "Summarize open support cases",
    "output": "Five support cases remain open",
    "executedBy": "support-automation",
    "environment": "Production"
  }'

AI Agents

OpenMetadata represents an AI agent in three related ways. Choose the interface that matches the task.

Task	Resource or Client	Purpose
Catalog and govern an agent	AI Application with `applicationType: Agent` or `MultiAgent`	Stores ownership, models, tools, data sources, governance metadata, and lifecycle state
Build and invoke an executable assistant	AI SDK	Manages AI Studio personas and agents, then invokes or streams responses
Record runtime activity	Agent Execution REST API	Stores status, model calls, tool calls, data access, metrics, and compliance checks

AI Studio agents and cataloged AI Applications aren’t the same resource. An integration can create both and relate operational activity to the cataloged application through the agent reference in each execution record.

Agent Execution Endpoints

Method	Endpoint	Description
`GET`	`/v1/agentExecutions`	List executions for an agent and time range
`GET`	`/v1/agentExecutions/{id}`	Retrieve an execution by ID
`POST`	`/v1/agentExecutions`	Create an execution record
`DELETE`	`/v1/agentExecutions/{agentId}/{timestamp}`	Delete the record for an agent and timestamp
`DELETE`	`/v1/agentExecutions/{id}`	Soft-delete or hard-delete a record by ID

The list endpoint accepts agentId, startTs, endTs, and limit. Required create fields are agent, agentId, timestamp, and status. Valid statuses are Running, Success, Failed, Timeout, Cancelled, and PartialSuccess.

Record an Execution

REST API

curl -X POST "{base_url}/api/v1/agentExecutions" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json" \
  -d '{
    "agent": {
      "id": "7f47e5f6-92bc-4a61-9cab-6bcc34b36c51",
      "type": "aiApplication",
      "name": "customer_support_agent",
      "fullyQualifiedName": "customer_support_agent"
    },
    "agentId": "7f47e5f6-92bc-4a61-9cab-6bcc34b36c51",
    "timestamp": 1787702400000,
    "endTimestamp": 1787702402400,
    "status": "Success",
    "input": "Summarize open support cases",
    "output": "Five support cases remain open",
    "executedBy": "support-automation",
    "environment": "Production"
  }'

The v2.0 Python and Java core SDKs have no typed Agent Execution client. Use the REST API to write or query execution records.

Invoke an AI Studio Agent

Install the Python AI SDK with pip install data-ai-sdk, then configure AI_SDK_HOST and AI_SDK_TOKEN.

Python AI SDK

from ai_sdk import AISdk, AISdkConfig

client = AISdk.from_config(AISdkConfig.from_env())
response = client.agent("DataQualityPlannerAgent").call(
    "Recommend data quality tests for the customers table"
)

print(response.response)
print(response.tools_used)

For persona and agent creation, TypeScript and Java examples, streaming, and multi-turn conversations, see the AI SDK guide.

API Reference

Data Assets

AI Assets

AI SDK

SDK

Discovery

Lineage

Data Contracts

Teams And Users

Data Governance

Data Quality

Main Concepts

AI Agents

AI Agents

Agent Execution Endpoints

Record an Execution

Invoke an AI Studio Agent

​AI Agents

​Agent Execution Endpoints

​Record an Execution

​Invoke an AI Studio Agent

AI Agents

Agent Execution Endpoints

Record an Execution

Invoke an AI Studio Agent