# Query serving endpoint > :::important This activity requires the USE CATALOG permission for Catalog `` on the connection. ::: ## Description :::important This activity requires the USE CATALOG permission for Catalog `` on the connection. ::: Databricks Agents securely connect your data with any AI model to create accurate, domain-specific applications. Through the Mosaic AI Gateway, agents that use many different frameworks can be deployed and assigned **serving endpoints** (Mosaic AI Model Serving). This activity enables the use of Databricks agents as participants in an automated process orchestrated by Maestro. ## Creating the Databricks Agent Generic LLM based agents, information extraction, and other types of agents within.Databricks can be used with the Databricks Agent connector as long as they are assigned and exposed via Serving endpoints. In most cases, integration with Maestro requires that the Agent render its output in a structured JSON schema. Information Extraction agents in Databricks are a good example. However, any agent can be prompted to respond in a well defined, simple schema using examples. ## How to use the activity To use this activity in a Maestro agentic process, follow these steps: 1. Add a service task element to the canvas and open the task's **Properties** panel. 2. Name the service task `Databricks Hello World`. 3. In the **Implementation** section, from the **Action** dropdown list, select **Start and wait for external agent**. 4. Select the **Databricks Agent** connector. 5. Select an existing connection or create a new one. For more information, see [Databricks Agent authentication](https://docs.uipath.com/integration-service/automation-cloud/latest/user-guide/uipath-databricks-databricks-authentication). 6. From **Activity**, select **Query serving endpoint**. 7. From **Serving Endpoint**, select an agent previously created in Databricks. 8. The **Messages** field is a complex input so it needs to be provided as an ARRAY and its elements as OBJECTS with nested properties. 1. Select the **Messages** field. 2. Select the right selector and choose **JSON editor**. 3. Select `{}` and change to ARRAY which will then show as `[]`. 4. Select `{x}` and select OBJECT. 5. Add (+) a nested element `*role*` as string with `"user"` as value. 6. Add (+) a nested element `*content*` as string, this is your prompt to the agent, with `"What is the capital of France"`. ![docs image](https://dev-assets.cms.uipath.com/assets/images/activities/latest-docs-image-632300-a1892c6a-ecd35920.webp) 9. Connect the start event to the service task, and the service task to an end event node on the canvas. 10. Select **Debug** to run this process. After a successful run, review the global variables and look for the **{:} response** from the source: **Databricks Hello World**. Take note of the structure of the reply. For example, this is the agent's response to the prompt "What can you do?": ``` { "id": "bf185700-c100-41be-9d4b-6a8aee2d8444", "databricks_output": { "databricks_request_id": "bf185700-c100-41be-9d4b-6a8aee2d8444" }, "messages": [ { "role": "assistant", "id": "run--38ced1fa-f810-49c2-87fc-e831e5ffb1d0-0", "content": "I can provide information and answer questions to the best of my ability. I can also execute Python code in a stateless sandboxed environment using the provided function. If you have a specific question or task in mind, feel free to ask and I'll do my best to assist you." } ] } ``` The agent’s output must be assigned to a process variable so it can influence the progress of the Maestro process, for example to make a decision based on a boolean evaluation, or to use the answer from a classification task. 1. In Design mode, select the agent from the design canvas. 2. In the **Properties** panel, select **Add new** and name the variable **agent_reponse**. 3. For **Value**, select **Databricks Hello World** > **Response** > **Message array** > **Message content (string)**. :::tip In practice, specify the structured output of your choice from the agent, and then evaluate the output within Maestro using the Expression editor to extract the specific part of the output you need in the type necessary for your process flow. ::: Example of handling agent output in Maestro using the Expression editor: If the prompt was: ``` "What is the capital of France?" answer in a JSON only on the form of {"capital":"Normandy") only JSON output ``` The response is **result.reponse.messages[0].content** (type `string`): ``` {"capital":"Paris"} ``` If we want to convert it to JSON, create a new variable of type JSON, for example `answer_in_JSON` and use the Expression editor: ``` js:JSON.parse(result.response.messages[0].content) ``` The result of the js expression is (type `JSON`): ``` { "capital": "Paris" } ``` ## Troubleshooting and Tuning Beyond establishing connectivity, you should test prompts both in the Databricks workspace as well as from Maestro. This ensures you achieve the desired output that can best be consumed by Maestro, assigned to variables, and passed on to other actors in the process. We recommend that detailed prompts remain within the **system prompts** of the agent within Databricks. The **user prompt** which is provided by Maestro to the agent at runtime should be brief and to the point. Its role is primarily to indicate the relevant variables needed by the agent to perform a specific tasks and generate an expected consistent output. Output that is aimed at humans, for example the reasons for an escalation, can be easily passed as natural text for the human. Output that is expected for an API/robot action must be strictly composed. Here is an example user prompt that yields a specific output from an agent. Use the C# Expression editor within Studio to add variables as needed. ``` "What is the quantity on inventory of Order ID " + vars.orderId_1 + "respond only with a JSON object with the quantity in the key Order_Quantity. No explanations, only JSON" ``` The agent will reply with: ``` {"Order_Quantity":"100"} ``` Pay special attention to types in your request to the agent and in the actual response. Even if the response looks like type `JSON`, it may actually be of type `string`.