--- title: Call Elastic Agent Builder agents from Elastic Workflows description: Learn how to invoke AI agents in Elastic Workflows using the `ai.agent` or `kibana.request` steps to add reasoning to your automated tasks. url: https://www.elastic.co/elastic/docs-builder/docs/3016/explore-analyze/ai-features/agent-builder/agents-and-workflows products: - Elastic Cloud Serverless - Elastic Observability - Elastic Security - Elasticsearch - Kibana applies_to: - Elastic Cloud Serverless: Preview - Elastic Stack: Preview since 9.3 --- # Call Elastic Agent Builder agents from Elastic Workflows Elastic Workflows and Elastic Agent Builder allow you to combine deterministic automation with conversational reasoning. By invoking an AI agent directly within a workflow execution, you can treat the agent as a "reasoning engine" that summarizes data, classifies events, or makes decisions before passing the results to the next step in your automation. This guide explains how to call an agent from a workflow. If you want to trigger a workflow in an agent conversation, you need to create a custom [workflow tool](https://www.elastic.co/elastic/docs-builder/docs/3016/explore-analyze/ai-features/agent-builder/tools/workflow-tools). ## Approaches There are two ways to integrate agents into your workflows: - **The `ai.agent` step:** A simplified shorthand step for common operations. Use this when you want to send a prompt to an agent and receive a text response without complex configuration. - **The `kibana.request` step:** A generic step that provides full access to the Elastic Agent Builder APIs. Use this for advanced use cases, such as listing available agents or managing agent sessions programmatically. ## Prerequisites Before you begin: - Familiarize yourself with the core concepts of [Elastic Workflows](https://www.elastic.co/elastic/docs-builder/docs/3016/explore-analyze/workflows). - Enable the Workflows feature in **Advanced settings**. - Ensure you have the correct privileges to create and run workflows. - For details, refer to [Set up workflows](https://www.elastic.co/elastic/docs-builder/docs/3016/explore-analyze/workflows/setup). - Create at least one workflow. ## Use the `ai.agent` step Follow these steps to invoke an `ai.agent` as a step within a workflow. 1. Open the **Workflows** editor and create or edit a workflow. 2. Add a new step with the type `ai.agent`. 3. Configure the **`agent_id`** parameter with the unique identifier of the target agent. 4. Configure the **`message`** parameter with your natural language prompt. 5. Optionally, configure the **`schema`** parameter with a JSON Schema object to receive structured output from the agent instead of free-text. ### Example: Analyze flight delays The following example demonstrates a workflow that searches for flight delays and uses the **Elastic AI Agent** to summarize the impact. To follow along with this example ensure that the [Kibana sample flight data](https://www.elastic.co/docs/extend/kibana/sample-data) is installed. ```yaml version: "1" name: analyze_flight_delays description: Fetches delayed flights and uses an agent to summarize the impact. enabled: true triggers: - type: manual steps: # Step 1: Get data from Elasticsearch - name: get_delayed_flights type: elasticsearch.search with: index: "kibana_sample_data_flights" query: range: FlightDelayMin: gt: 60 size: 5 # Step 2: Ask the agent to reason over the data - name: summarize_delays type: ai.agent with: agent_id: "elastic-ai-agent" message: | Review the following flight delay records and summarize which airlines are most affected and the average delay time: {{ steps.get_delayed_flights.output }} # Step 3: Print the agent's summary - name: print_summary type: console with: message: "{{ steps.summarize_delays.output }}" ``` ### Parameters Use the following parameters in the `with` block to configure the step: | Parameter | Type | Required | Description | |------------|--------|----------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------| | `agent_id` | string | Yes | The unique identifier of the target agent (must exist in Elastic Agent Builder). | | `message` | string | Yes | The natural language prompt to send to the agent. Can include template variables to reference data from previous steps. | | `schema` | object | No | A JSON Schema object that defines the structure of the expected response. When provided, the agent returns structured data matching the schema instead of free-text. | ## Use `kibana.request` step Use the generic `kibana.request` step to interact with Elastic Agent Builder APIs programmatically. 1. Add a new step with the type `kibana.request`. 2. Set the method (for example: `GET`, `POST`). 3. Set the `path` to the specific [Agent Builder API endpoint](https://www.elastic.co/docs/api/doc/kibana/group/endpoint-agent-builder). ### Example: List available agents This step retrieves a list of all agents currently available in Agent Builder. ```yaml name: list_agents enabled: true triggers: - type: manual steps: - name: list_agents type: kibana.request with: method: GET path: /api/agent_builder/agents ``` ## Examples The [`elastic/workflows` GitHub repo](https://github.com/elastic/workflows) contains more than 50 examples you can use as a starting point. ## Related pages - [Tools overview](https://www.elastic.co/elastic/docs-builder/docs/3016/explore-analyze/ai-features/agent-builder/tools) - [Workflow tools](https://www.elastic.co/elastic/docs-builder/docs/3016/explore-analyze/ai-features/agent-builder/tools/workflow-tools) - [Workflows](https://www.elastic.co/elastic/docs-builder/docs/3016/explore-analyze/workflows) - [Agent Builder API](https://www.elastic.co/docs/api/doc/kibana/group/endpoint-agent-builder)