Move GenAI docs to top-level folder

content/en/docs/control-center/entitlements/cloud-tokens.md

@@ -98,7 +98,7 @@ These tables show how many Mendix Cloud Tokens each CRP requires:
 
 ## GenAI Resource Packs {#grps}
 
-GenAI Resource Packs provide turn-key access to Generative AI technology, delivered through Mendix Cloud. For the technical details of each GenAI Resource Pack (GRP), refer to [GenAI Resource Packs](/appstore/modules/genai/mx-cloud-genai/resource-packs/).
+GenAI Resource Packs provide turn-key access to Generative AI technology, delivered through Mendix Cloud. For the technical details of each GenAI Resource Pack (GRP), refer to [GenAI Resource Packs](/agents/mx-cloud-genai/resource-packs/).
 
 | GenAI Model Resource Pack – Anthropic Claude Sonnet | Mendix Cloud Tokens |
 | --- | --: |

content/en/docs/control-center/genai-resources-self-service.md

@@ -7,7 +7,7 @@ weight: 20
 
 ## Introduction
 
-The **GenAI Resources** section provides a detailed overview of all Mendix GenAI resources available within your company, allowing Mendix Admins to seamlessly provision and deprovision GenAI resources as needed. With this feature, Mendix Admins can efficiently manage all GenAI resources directly within the [Control Center](https://controlcenter.mendix.com/index.html) through a self-service capability, ensuring streamlined operations and improved governance. For more information, refer to [Accessing GenAI Resources](/appstore/modules/genai/mx-cloud-genai/resource-packs/#accessing-genai-resources).
+The **GenAI Resources** section provides a detailed overview of all Mendix GenAI resources available within your company, allowing Mendix Admins to seamlessly provision and deprovision GenAI resources as needed. With this feature, Mendix Admins can efficiently manage all GenAI resources directly within the [Control Center](https://controlcenter.mendix.com/index.html) through a self-service capability, ensuring streamlined operations and improved governance. For more information, refer to [Accessing GenAI Resources](/agents/mx-cloud-genai/resource-packs/#accessing-genai-resources).
 
 ## Prerequisites
 
@@ -44,13 +44,13 @@ When provisioning a new resource, enter the following information:
 * **Display Name** – The name of the resource.
 * **Environment** – The environment for which the resource is created, such as Test, Acceptance, or Production.
 * **Mendix Cloud Region** – The cloud region where the resource will be hosted.
-* **Cross-region inference** – Specifies whether the selected model supports cross-region inference. For more information, refer to the [Settings](/appstore/modules/genai/mx-cloud-genai/Navigate-MxGenAI/#settings) section of *Navigate through the Mendix Cloud GenAI Portal*.
+* **Cross-region inference** – Specifies whether the selected model supports cross-region inference. For more information, refer to the [Settings](/agents/mx-cloud-genai/Navigate-MxGenAI/#settings) section of *Navigate through the Mendix Cloud GenAI Portal*.
 * **Available Text Generation Models** – A list of the supported models you can choose from, for example, Anthropic Claude Sonnet V4.
 * **Size** – The subscription plan with the tokens used for resources. 
 * **User** – The name of the user for whom the provisioning was initially created.
 * **Email** – The user's email address.
 
-After filling in the required fields, you can review all the entered details in the **Resource Specification**. To learn more, refer to [Mendix Cloud GenAI Resource Packs](/appstore/modules/genai/mx-cloud-genai/resource-packs/).
+After filling in the required fields, you can review all the entered details in the **Resource Specification**. To learn more, refer to [Mendix Cloud GenAI Resource Packs](/agents/mx-cloud-genai/resource-packs/).
 
 Click **Provision Resource** to finalize the process. You are taken back to the **GenAI Resources** page, where the newly created resource is displayed in the list. Selecting the newly provisioned resource opens its details directly in the Mendix Cloud GenAI Portal in a new tab.
 

content/en/docs/marketplace/genai/_index.md → content/en/docs/genai/_index.md

@@ -1,9 +1,11 @@
 ---
 title: "Enrich Your Mendix App with Agentic Capabilities"
-url: /appstore/modules/genai/
+url: /agents/
 linktitle: "Agents"
 description: "Describes how to integrate agentic and generative AI into Mendix applications using Agents Kit components. Provides a catalog of available starter apps, showcase apps, connectors, modules, and models."
 weight: 7
+aliases:
+    - /appstore/modules/genai/
 ---
 
 ## Introduction
@@ -24,10 +26,10 @@ These pages focus on integrating agentic and generative AI into applications usi
 
 Start using AI capabilities based on your experience level:
 
-* **Familiar with generative AI?** Start building with the [How to Build Smarter Apps Using GenAI](/appstore/modules/genai/how-to/) guides.
+* **Familiar with generative AI?** Start building with the [How to Build Smarter Apps Using GenAI](/agents/how-to/) guides.
 * **New to generative AI?** Follow these steps:
 
-    1. Familiarize yourself with the [core concepts](/appstore/modules/genai/get-started/), including prompt engineering, retrieval augmented generation (RAG), and function calling (ReAct).
+    1. Familiarize yourself with the [core concepts](/agents/get-started/), including prompt engineering, retrieval augmented generation (RAG), and function calling (ReAct).
     2. Choose an architecture for your use case. See the [Components and Models](#architecture) section for available options.
     3. Obtain the required credentials for your selected architecture.
 
@@ -57,10 +59,10 @@ Integrate AI capabilities into your applications with Agents Kit, a collection o
 
 | Asset | Description | Studio Pro Version |
 | --- | --- | --- |
-| [Agent Commons](/appstore/modules/genai/genai-for-mx/agent-commons/) | Build agentic functionality by defining, testing, and evaluating agents at runtime. Iterate on prompts and agent configurations without app redeployment through the integrated Agent Builder UI. | 10.24 |
-| [Agent Editor](/appstore/modules/genai/genai-for-mx/agent-editor/) | Define agents as version-controlled documents in Studio Pro at design time. Author prompts, configure tools and knowledge bases, test locally, and deploy agents as part of your app model. | 11.9 |
-| [Conversational UI](/appstore/modules/genai/conversational-ui/) | Create chat interfaces for full-screen, sidebar, or modal GenAI conversations. Monitor token consumption and trace interactions with UI features built on GenAI Commons. | 10.24 |
-| [GenAI Commons](/appstore/modules/genai/commons/) | Use common capabilities that allow all GenAI connectors to be integrated with the other modules. You can also implement your own connector based on this module. | 10.24 |
+| [Agent Commons](/agents/genai-for-mx/agent-commons/) | Build agentic functionality by defining, testing, and evaluating agents at runtime. Iterate on prompts and agent configurations without app redeployment through the integrated Agent Builder UI. | 10.24 |
+| [Agent Editor](/agents/genai-for-mx/agent-editor/) | Define agents as version-controlled documents in Studio Pro at design time. Author prompts, configure tools and knowledge bases, test locally, and deploy agents as part of your app model. | 11.9 |
+| [Conversational UI](/agents/genai-for-mx/conversational-ui/) | Create chat interfaces for full-screen, sidebar, or modal GenAI conversations. Monitor token consumption and trace interactions with UI features built on GenAI Commons. | 10.24 |
+| [GenAI Commons](/agents/genai-for-mx/commons/) | Use common capabilities that allow all GenAI connectors to be integrated with the other modules. You can also implement your own connector based on this module. | 10.24 |
 
 #### Connector Modules {#connectors}
 
@@ -69,18 +71,18 @@ All connectors depend on GenAI Commons and can be used with the other [core modu
 | Asset | Description | Studio Pro Version |
 | --- | --- | --- |
 | [Amazon Bedrock Connector](/appstore/modules/aws/amazon-bedrock/) | Connect to Amazon Bedrock. | 10.24 |
-| [Google Gemini Connector](/appstore/modules/genai/reference-guide/external-connectors/gemini/) | Connect to Google Gemini. | 10.24 |
-| [Mendix Cloud GenAI Connector](/appstore/modules/genai/mx-cloud-genai/MxGenAI-connector/) | Connect to Mendix Cloud and use Mendix Cloud GenAI resource packs directly within your Mendix application. | 10.24 |
-| [Mistral Connector](/appstore/modules/genai/reference-guide/external-connectors/mistral/) | Connect to Mistral AI. | 10.24 |
-| [OpenAI Connector](/appstore/modules/genai/openai/) | Connect to OpenAI and Microsoft Foundry. | 10.24 |
-| [PgVector Knowledge Base](/appstore/modules/genai/pgvector/) | Manage and interact with a PostgreSQL PgVector knowledge base. | 10.24 |
+| [Google Gemini Connector](/agents/reference-guide/external-connectors/gemini/) | Connect to Google Gemini. | 10.24 |
+| [Mendix Cloud GenAI Connector](/agents/mx-cloud-genai/mxgenai-connector/) | Connect to Mendix Cloud and use Mendix Cloud GenAI resource packs directly within your Mendix application. | 10.24 |
+| [Mistral Connector](/agents/reference-guide/external-connectors/mistral/) | Connect to Mistral AI. | 10.24 |
+| [OpenAI Connector](/agents/reference-guide/external-connectors/openai/) | Connect to OpenAI and Microsoft Foundry. | 10.24 |
+| [PgVector Knowledge Base](/agents/reference-guide/external-connectors/pgvector/) | Manage and interact with a PostgreSQL PgVector knowledge base. | 10.24 |
 
 #### MCP Modules {#mcp-modules}
 
 | Asset | Description | Studio Pro Version |
 | --- | --- | --- |
-| [MCP Client](/appstore/modules/genai/mcp-modules/mcp-client/) | Access tools and prompts available via MCP inside your Mendix app and add them to LLM requests. | 10.24 |
-| [MCP Server](/appstore/modules/genai/mcp-modules/mcp-server/) | Make your Mendix business logic available to any agent in your enterprise landscape. Expose reusable prompts, including the ability to use prompt variables. List and run actions implemented in the application as a tool. | 10.24 |
+| [MCP Client](/agents/mcp-modules/mcp-client/) | Access tools and prompts available via MCP inside your Mendix app and add them to LLM requests. | 10.24 |
+| [MCP Server](/agents/mcp-modules/mcp-server/) | Make your Mendix business logic available to any agent in your enterprise landscape. Expose reusable prompts, including the ability to use prompt variables. List and run actions implemented in the application as a tool. | 10.24 |
 
 {{% alert color="info" %}}
 Older versions of the modules and the GenAI Showcase App are available in Studio Pro 9.24.2.
@@ -94,8 +96,8 @@ Mendix [connectors](#connectors) offer direct support for the following models.
 
 | Models | Category | Input | Output | Additional Capabilities |
 | --- | --- | --- | --- | --- |
-| [Anthropic Claude Sonnet Models](/appstore/modules/genai/mx-cloud-genai/resource-packs/#supported-models) | Chat completions | text, image, document | text | Function calling |
-| [Cohere Embed Models](/appstore/modules/genai/mx-cloud-genai/resource-packs/#supported-models) | Embeddings | text | embeddings | |
+| [Anthropic Claude Sonnet Models](/agents/mx-cloud-genai/resource-packs/#supported-models) | Chat completions | text, image, document | text | Function calling |
+| [Cohere Embed Models](/agents/mx-cloud-genai/resource-packs/#supported-models) | Embeddings | text | embeddings | |
 
 #### Microsoft Foundry (OpenAI) / OpenAI
 
@@ -151,4 +153,4 @@ In addition to the models listed above, you can also connect to other models by
 
 * To connect to other [foundation models](https://docs.aws.amazon.com/bedrock/latest/userguide/models-features.html) and implement them in your app, use the [Amazon Bedrock connector](/appstore/modules/aws/amazon-bedrock/).
 * To connect to [Snowflake Cortex LLM](https://docs.snowflake.com/en/sql-reference/functions/complete-snowflake-cortex) functions, [configure the Snowflake AI Data Connector for Snowflake Cortex Analyst](/appstore/connectors/snowflake/snowflake-ai-data-connector/#cortex-analyst).
-* To implement your own connector that is compatible with the other components, use the [GenAI Commons](/appstore/modules/genai/commons/) interface and see [How to Build Your Own GenAI Connector](/appstore/modules/genai/how-to/byo-connector/).
+* To implement your own connector that is compatible with the other components, use the [GenAI Commons](/agents/genai-for-mx/commons/) interface and see [How to Build Your Own GenAI Connector](/agents/how-to/byo-connector/).

content/en/docs/marketplace/genai/concepts/_index.md → content/en/docs/genai/concepts/_index.md

@@ -1,11 +1,12 @@
 ---
 title: "GenAI Concepts"
-url: /appstore/modules/genai/get-started/
+url: /agents/get-started/
 linktitle: "GenAI Concepts"
 weight: 10
 description: "Describes the concepts behind generative AI and what you might implement with it."
 aliases:
     - /appstore/modules/genai/using-gen-ai/ 
+    - /appstore/modules/genai/get-started/
 ---
 
 ## Introduction
@@ -41,7 +42,7 @@ For example, you can use an LLM to do:
     * Translate languages
     * Simulate characters for games
 
-Some LLMs, such as [Anthropic Claude](/appstore/modules/aws/amazon-bedrock/) and [GPT-4o](/appstore/modules/genai/openai/), can also use one or more images as input, allowing you to ask questions about images for use cases such as object recognition, image to text (OCR), and validating whether an image is as intended.
+Some LLMs, such as [Anthropic Claude](/appstore/modules/aws/amazon-bedrock/) and [GPT-4o](/agents/reference-guide/external-connectors/openai/), can also use one or more images as input, allowing you to ask questions about images for use cases such as object recognition, image to text (OCR), and validating whether an image is as intended.
 
 #### Embeddings Generation
 
@@ -64,7 +65,7 @@ Adding knowledge bases helps to tailor response generation to specific contexts
 
 Knowledge bases are often used for:
 
-1. [Retrieval Augmented Generation (RAG)](/appstore/modules/genai/rag/) retrieves relevant knowledge from the knowledge base, incorporates it into a prompt, and sends it to the model to generate a response.
+1. [Retrieval Augmented Generation (RAG)](/agents/rag/) retrieves relevant knowledge from the knowledge base, incorporates it into a prompt, and sends it to the model to generate a response.
 2. Semantic search enables advanced search capabilities by considering the semantic meaning of the text, going beyond exact and approximate matching. It allows the knowledge base to be searched for similar chunks effectively.
 
 ### What is an LLM Not?
@@ -95,7 +96,7 @@ Prompt engineering is the activity of designing the input text that will be sent
 * the relevant input data (from the end-user or passed from a microflow)
 * the requested output structure (for example, tone of voice or a JSON format)
 
-With prompt engineering you can guide the model to generate accurate, applicable, and coherent responses. The quality of your prompts directly influences the quality of the response. See [Prompt Engineering](/appstore/modules/genai/prompt-engineering/) to learn more about prompt engineering.
+With prompt engineering you can guide the model to generate accurate, applicable, and coherent responses. The quality of your prompts directly influences the quality of the response. See [Prompt Engineering](/agents/prompt-engineering/) to learn more about prompt engineering.
 
 ## Retrieval Augmented Generation (RAG) {#rag}
 
@@ -124,24 +125,24 @@ For example, Amazon Bedrock has the concept of [knowledge bases for Amazon Bedro
 
 If your chosen architecture does not have fully-integrated RAG capabilities, or if you want tighter control of the RAG process, you can create and use your own knowledge base.
 
-In this case you will have to index and store your knowledge yourself, and index your input data in order to retrieve the information with which you want to augment your prompt. For this you can use the [PgVector Knowledge Base module](/appstore/modules/genai/pgvector/) in combination with an embeddings model, to maintain and use your knowledge base. 
+In this case you will have to index and store your knowledge yourself, and index your input data in order to retrieve the information with which you want to augment your prompt. For this you can use the [PgVector Knowledge Base module](/agents/reference-guide/external-connectors/pgvector/) in combination with an embeddings model, to maintain and use your knowledge base. 
 
-An example of how this can be done with OpenAI is described in [RAG Example Implementation in the GenAI Showcase App](/appstore/modules/genai/rag/).
+An example of how this can be done with OpenAI is described in [RAG Example Implementation in the GenAI Showcase App](/agents/rag/).
 
 ### The ReAct Pattern (Function Calling) {#react}
 
 Another way to provide the LLM with additional information and capabilities is to use function calling, also known as tool use. With function calling you can make specific microflows available to the LLM. While evaluating the prompt, the LLM will, optionally, ask to execute a particular microflow. The Mendix application will execute this microflow and return additional information for the LLM to add to the prompt being processed.
 
 This microflow runs in the context of the user, allowing you to make sure that it only shows data that is relevant for the current user. You can also use it to execute actions on behalf of the user, or interact with page that the user is looking at.
 
-See [Function Calling](/appstore/modules/genai/function-calling/) for more information on ReAct. You can see ReAct implemented in the [GenAI Showcase App](https://marketplace.mendix.com/link/component/220475) where the `GetInformationForTicketID` microflow allows an LLM to answer a question like "What is the status of ticket 42?".
+See [Function Calling](/agents/function-calling/) for more information on ReAct. You can see ReAct implemented in the [GenAI Showcase App](https://marketplace.mendix.com/link/component/220475) where the `GetInformationForTicketID` microflow allows an LLM to answer a question like "What is the status of ticket 42?".
 
 This pattern is supported both by [OpenAI](https://platform.openai.com/docs/guides/function-calling) and [various models available on Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html#conversation-inference-supported-models-features).
 
 ## Agents and Assistants {#agents}
 
 The agent concept combines prompts, RAG (Retrieval Augmented Generation), and ReAct patterns in a single call. These components of agent-based logic are all supported by our Agents Kit. Using LLMs, business logic can be enriched by enabling AI agents to reason and autonomously execute actions while being grounded in domain-specific knowledge. With Mendix's Agents Kit, agents become a seamless part of your application's logic.
 
-For an overview of the components that help you get started, refer to [the Agents Kit overview](/appstore/modules/genai/#architecture). 
+For an overview of the components that help you get started, refer to [the Agents Kit overview](/agents/#architecture). 
 
 In addition, you can integrate agentic behavior in a Mendix app by leveraging external agents through cloud infrastructure providers. In this case, the Mendix app does not store the agent definition. Instead, it only calls the external agent. For example, [Agents for Amazon Bedrock](https://aws.amazon.com/bedrock/agents/) provides this functionality for Amazon Bedrock. You can find out how to use this in your Mendix application in [Invoking an Agent with the InvokeAgent Operation](/appstore/modules/aws/amazon-bedrock/#invokeagent) section of the *Amazon Bedrock* module documentation.