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content/en/docs/marketplace/genai/reference-guide/agent-editor.md

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@@ -131,7 +131,7 @@ Defining an agent is also document-based and can be configured using the Agent e
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* Add an Agent document from the **App Explorer** at the module level. Right-click on the module or folder where you want to create your Model document, select **Add other** and find Agent in the bottom section.
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* Select a Module document for an agent to call a text generation resource.
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* Configure the **System prompt** and, additionally for task-style execution, the **User prompt**. In these prompts, define placeholders with double braces (for example, `{{variable}}`).
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* Configure the **System prompt**, and additionally define a **User prompt** for task-style execution. In both prompts include placeholders with double braces, for example, `{{variable}}`.
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* When placeholders are used, select a **Context entity** to resolve values at runtime. The placeholders used within the prompts need to match with the attribute names of the entity selected, so that attribute values can be inserted instead of the placeholders at runtime.
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* Optionally, adjust the **Model settings** as needed (maximum tokens, temperature, and TopP), based on the supported ranges of the model provider.
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When configuring the action, select the Agent document so that the right agent is called. If your prompts use variable placeholders, pass a context object to the action. This object must be of the selected context entity type so that placeholders can be resolved at runtime.
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For **Call Agent without History**, you can optionally pass a `Request` object to set request-level values, and a `FileCollection` object with files to send along with the user message to make use of vision or document chat capabilities. For **Call Agent with History** the `Request` object is mandatory, as it contains any previous messages from the conversation. Support for files and images depends on the underlying large language model. Refer to the documentation of the specific connector.
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For **Call Agent without History**, you can optionally pass a `Request` object to set request-level values, and a `FileCollection` object with files to send along with the user message to make use of vision or document chat capabilities. For **Call Agent with History** the `Request` object is mandatory, as it contains the previous messages from the conversation. Support for files and images depends on the underlying large language model. Refer to the documentation of the specific connector.
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The output is a `GenAICommons.Response` object, aligned with the GenAI Commons and Agent Commons domain models and actions, which can be used for further logic. Additionally, all agents created via the Agent Editor extension are seamlessly integrated with other Mendix offerings, such as the [Token consumption monitor](/appstore/modules/genai/genai-for-mx/conversational-ui/#snippet-token-monitor) or the [Traceability](/appstore/modules/genai/genai-for-mx/conversational-ui/#traceability) feature from [ConversationalUI](/appstore/modules/genai/genai-for-mx/conversational-ui/).
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### Including the Agent in a Conversational User Interface (#conversational-ui)
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Pages and Snippets as building blocks for chat-type UI patterns exist in the [ConversationalUI module](/appstore/modules/genai/genai-for-mx/conversational-ui/). The central entity here is the `ChatContext` which represents a user-agent chat session. When using the Agent Editor, in order to instantiate a new `ChatContext`, you can use the **New Chat for Agent** action in the microflow that opens your chat page and pass the Agent document. Read more about the conversational UI patterns in the [documentation](/appstore/modules/genai/genai-for-mx/conversational-ui/#chat-context-operations).
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Pages and Snippets are building blocks for chat-type UI patterns exist in the [ConversationalUI module](/appstore/modules/genai/genai-for-mx/conversational-ui/). The central entity here is the `ChatContext` which represents a user-agent chat session. When using the Agent Editor, in order to instantiate a new `ChatContext`, use the **New Chat for Agent** action in the microflow that opens your chat page and passes the Agent document. For more details, refer to the [Conversational UI patterns](/appstore/modules/genai/genai-for-mx/conversational-ui/#chat-context-operations).
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### Deploying the Agent to Cloud Environments {#deploy-agent}
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## Known Limitations {#limitations}
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* Currently, the Agent Editor supports only Mendix Cloud GenAI as a provider for text generation models and knowledge bases. Support for other providers, such as (Azure) OpenAI and Amazon Bedrock, is planned for a future release.
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* Support for Mac users is limited: some functionalities might not work, such as doing a test call for Model documents. Mendix recommends using Studio Pro on Windows to use all features of the Agent Editor smoothly.
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* Support for Mac users is limited. Some functionalities might not work, such as doing a test call for Model documents. Mendix recommends using Studio Pro on Windows to use all features of the Agent Editor smoothly.
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* MCP tool support is limited to whole-server integration. Selecting individual tools from a consumed MCP service to be added to an agent is not yet supported. That also means that the tool choice option `Tool` can only refer to a microflow tool currently.
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* If a document that is referenced by an Agent document is excluded, Studio Pro shows a consistency error accordingly. In the current version, these consistency errors may not be resolved automatically when the excluded document is included again. You can resolve it by synchronizing the project directory (<kbd>F4</kbd>) or by making a small change in any agent-related document (for example, add a character to a system prompt and remove it again).
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* The extension creates a `/agenteditor` log folder in the app directory. This is not excluded from version control automatically upon including the module from the Marketplace. This folder should be added to `.gitignore` manually as described in the [First-time setup](#setup) section.

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