Version Agents Kit

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content/en/docs/genai/_index.md

@@ -12,7 +12,9 @@ aliases:
 
 With Mendix's agentic capabilities, you can build AI-powered features into your applications using leading AI models and your own data.
 
-Mendix supports a variety of agentic and generative AI capabilities that you can integrate into your applications. Some typical use cases include the following:
+Integrate AI capabilities into your applications with Agents Kit, a collection of Mendix starter apps, connectors, and modules that support implementations from simple text generation to complex multi-step agentic workflows. [Agents Kit 2](/agents/agents-kit-2/) is available for Mendix Studio Pro 11.12 and above. [Agents Kit 1](/agents/agents-kit-1/) is available for Studio Pro 10.24 and above. Older versions of some Marketplace modules and the GenAI Showcase App are available in Studio Pro 9.24.2.
+
+Some typical use cases include the following:
 
 * Create AI agents that autonomously interact with your Mendix app's data, logic, and external systems.
 * Build conversational UIs with human-in-the-loop controls and embed AI-powered interactions directly into your Mendix applications.
@@ -26,80 +28,25 @@ 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](/agents/how-to/) guides.
+* **Familiar with generative AI?** Start building with the [How to Build Smarter Apps Using GenAI](/agents/agents-kit-2/how-to/) guides.
 * **New to generative AI?** Follow these steps:
 
     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.
+    2. Choose an architecture for your use case.
     3. Obtain the required credentials for your selected architecture.
 
-## Components and Models {#architecture}
-
-Integrate AI capabilities into your applications with Agents Kit, a collection of Mendix starter apps, connectors, and modules that support implementations from simple text generation to complex multi-step agentic workflows. The following sections describe the components available in the kit as well as the available models.
-
-### Agents Kit Components 
-
-#### Starter Apps {#starter-apps}
-
-| Asset | Description | Studio Pro Version |
-| --- | --- | --- |
-| [Agent Builder Starter App](https://marketplace.mendix.com/link/component/240369) (formerly known as Support Assistant Starter App) | Build agentic apps with this starter app that includes Agent Commons and all its required dependencies. Includes a working conversational support agent that you can customize with prompts, tool calling, knowledge base integration, and human-in-the-loop capabilities. | 10.24 |
-| [AI Bot Starter App](https://marketplace.mendix.com/link/component/227926) | Build your own enterprise-grade ChatGPT-like app. Connect to a supported model and write custom instructions to create a chatbot that can support use cases such as brainstorming, copywriting, document analysis, or coding support. | 10.24 |
-| [Blank GenAI App](https://marketplace.mendix.com/link/component/227934) | Start building with Mendix GenAI capabilities using this blank starter app that comes preloaded with connectors for Mendix Cloud GenAI, OpenAI, Amazon Bedrock, and Mistral, plus Agent Commons and all its required dependencies. | 10.24 |
-| [RFP Assistant Starter App / Questionnaire Assistant Starter App](https://marketplace.mendix.com/link/component/235917) | Demonstrates a time-saving GenAI pattern for answering similar-but-different questions. Upload Request for Proposal (RFP) documents, generate responses from a historical knowledge base of question-answer pairs, edit with AI assistance, and keep the model's responses current with continuous knowledge base updates. | 10.24 |
-
-#### Showcase Apps {#showcase-apps}
-
-| Asset | Description | Studio Pro Version |
-| --- | --- | --- |
-| [GenAI Showcase App](https://marketplace.mendix.com/link/component/220475) | Explore example use cases for Agents Kit connectors and modules, including multi-agent patterns, exposing and consuming tools via MCP, interactive chatbots, RAG, function calling, image generation, and semantic search. | 10.24 |
-| [Snowflake Showcase App](https://marketplace.mendix.com/link/component/225845) | Learn how to use Snowflake connectors to read and write data, leverage Snowflake Cortex ML and LLM capabilities, chat with structured data using Cortex Analyst, and implement role-based access control. | 10.24 |
-
-#### Core Modules {#core-modules}
-
-| Asset | Description | Studio Pro Version |
-| --- | --- | --- |
-| [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/) | Integrate GenAI connectors with other modules using common capabilities provided by this base module. Required dependency for both core and connector modules. You can also implement your own connector based on this module. | 10.24 |
-
-#### Connector Modules {#connectors}
-
-All connectors depend on GenAI Commons and can be used with the other [core modules](#core-modules) to connect to conversation endpoints.
-
-| Asset | Description | Studio Pro Version |
-| --- | --- | --- |
-| [Amazon Bedrock Connector](/appstore/modules/aws/amazon-bedrock/) | Connect to Amazon Bedrock. | 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](/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.
-{{% /alert %}}
-
-### Available Models {#models}
+## Available Models {#models}
 
-Mendix [connectors](#connectors) offer direct support for the following models.
+Mendix [connectors](/agents/agents-kit-2/#connectors) offer direct support for the following models.
 
-#### Mendix Cloud GenAI
+### Mendix Cloud GenAI
 
 | Models | Category | Input | Output | Additional Capabilities |
 | --- | --- | --- | --- | --- |
 | [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
+### Microsoft Foundry (OpenAI) / OpenAI
 
 | Models | Category | Input | Output | Additional Capabilities |
 | --- | --- | --- | --- | --- |
@@ -109,7 +56,7 @@ Mendix [connectors](#connectors) offer direct support for the following models.
 
 For a list of all OpenAI models, see [Models](https://developers.openai.com/api/docs/models) in the OpenAI documentation.
 
-#### Mistral
+### Mistral
 
 | Models | Category | Input | Output | Additional Capabilities |
 | --- | --- | --- | --- | --- |
@@ -120,14 +67,14 @@ For a list of all OpenAI models, see [Models](https://developers.openai.com/api/
 
 For a list of all Mistral models, see [Models Overview](https://docs.mistral.ai/models/overview) in the Mistral documentation.
 
-#### Google Gemini
+### Google Gemini
 
 | Models | Category | Input | Output | Additional Capabilities |
 | --- | --- | --- | --- | --- |
 | Gemini 2.5 Flash, Gemini 2.5 Flash-Lite, Gemini 2.5 Pro, Gemini Flash Latest, Gemini Flash-Lite Latest, Gemini Pro Latest | Chat completions | text, image | text | Function calling |
 | Gemini 3 Flash Preview, Gemini 3.1 Flash-Lite, Gemini 3.1 Pro Preview, Gemini 3.5 Flash | Chat completions | text, image | text | |
 
-#### Amazon Bedrock
+### Amazon Bedrock
 
 | Models | Category | Input | Output | Additional Capabilities |
 | --- | --- | --- | --- | --- |
@@ -141,10 +88,10 @@ For embeddings and image generation, models that support the Invoke API but lack
 
 For a list of all Bedrock Models, see [Models at a glance](https://docs.aws.amazon.com/bedrock/latest/userguide/model-cards.html). To determine if a model supports the Converse or Invoke APIs, see the model details after selecting a model from the list.
 
-#### Connecting to Other Models
+### Connecting to Other Models
 
 In addition to the models listed above, you can also connect to other models by implementing one of the following options:
 
 * 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](/agents/genai-for-mx/commons/) interface and see [How to Build Your Own GenAI Connector](/agents/how-to/byo-connector/).
+* To implement your own connector that is compatible with the other components, use the [GenAI Commons](/agents/agents-kit-2/reference-guide/genai-for-mx/commons/) interface and see [How to Build Your Own GenAI Connector](/agents/agents-kit-2/how-to/byo-connector/).

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

@@ -42,7 +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](/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.
+Some LLMs, such as [Anthropic Claude](/appstore/modules/aws/amazon-bedrock/) and [GPT-4o](/agents/agents-kit-2/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
 
@@ -75,9 +75,9 @@ While LLMs are powerful, they are not without limitations. Remember they are:
 * **not conscious:** LLMs do not possess self-awareness or semantic knowledge (understanding). They generate text based on patterns in the data they were trained on.
 * **not perfect:** These models can sometimes produce incorrect or nonsensical outputs (so-called hallucinations), especially if the input is ambiguous or if they were not trained on the relevant data.
 * **not a replacement for human judgment:** LLMs should be seen as tools to augment human capabilities, not replace human expertise or critical thinking.
-* **not trained for specific use cases**: LLMs are trained on a broad variety of use cases, for some specific (e.g. statistical) use cases you need to use traditional machine learning (ML) models. For more details on how to deploy such a model see [Machine Learning Kit](/refguide/machine-learning-kit/).
+* **not trained for specific use cases**: LLMs are trained on a broad variety of use cases, for some specific (such as statistical) use cases you need to use traditional machine learning (ML) models. For more details on how to deploy such a model see [Machine Learning Kit](/refguide/machine-learning-kit/).
 
-### Making an LLM more specific
+### Making an LLM more Specific
 
 Since an LLM is pretrained on a huge dataset it can do many things out of the box. If you want to make it more specific to your use case and program it to perform specific functions in your apps, you can typically do three things:
 
@@ -100,7 +100,7 @@ With prompt engineering you can guide the model to generate accurate, applicable
 
 ## Retrieval Augmented Generation (RAG) {#rag}
 
-The knowledge of LLMs is limited to the data they have been trained on. This is generally-available information, for example from Wikipedia and other internet sources.
+The knowledge of LLMs is limited to the data they have been trained on. This is generally available information, for example from Wikipedia and other internet sources.
 
 For use cases where the LLM needs to be aware of domain-specific or private enterprise data, you can use the RAG pattern. This allows you to add large amounts of additional context to a request without making the prompts extremely lengthy. To implement RAG, you need to set up a knowledge base that contains the data. When evaluating the actual user prompt, the basic pattern of RAG consists of three phases:
 
@@ -119,13 +119,13 @@ There are two approaches to including RAG in your generative AI-powered app.
 
 Some architectures provide the capabilities for the RAG pattern out of the box, which shields you from having to retrieve and augment your prompt yourself. All you need to do is ensure that your knowledge base is available to the model.
 
-For example, Amazon Bedrock has the concept of [knowledge bases for Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html), which allows you to create a repository of private information that can be used to improve an LLM's response. This knowledge base is based on files (e.g. manuals or historical documents) in an S3 bucket. You can then use the Retrieve And Generate operation which will retrieve data from the knowledge base, augment the prompt with the retrieved information, and generate the response.
+For example, Amazon Bedrock has the concept of [knowledge bases for Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html), which allows you to create a repository of private information that can be used to improve an LLM's response. This knowledge base is based on files (such as manuals or historical documents) in an S3 bucket. You can then use the Retrieve And Generate operation which will retrieve data from the knowledge base, augment the prompt with the retrieved information, and generate the response.
 
 ### PgVector Knowledge Base {#pgvectorknowledgebase}
 
 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](/agents/reference-guide/external-connectors/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/agents-kit-2/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](/agents/rag/).
 
@@ -143,6 +143,6 @@ This pattern is supported both by [OpenAI](https://platform.openai.com/docs/guid
 
 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](/agents/#architecture). 
+For an overview of the components that help you get started, refer to [Agents Kit Components](/agents/agents-kit-2/#components). 
 
 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.

content/en/docs/genai/concepts/agents.md

@@ -12,7 +12,7 @@ aliases:
 
 GenAI agents are autonomous computational systems that perform actions in response to triggers such as user input or system events. These agents apply reasoning, execute tools (functions), and leverage data from knowledge bases to determine the most appropriate responses. They may be adaptive (learning-based) or task-specific, designed to automate processes and improve operational efficiency.
 
-If you are interested in creating your own agent, explore the guide on [Creating Your First Agent](/agents/how-to/creating-agents/). It walks you through how to combine prompt engineering, function calling, and knowledge base integration—all within a Mendix app.
+If you are interested in creating your own agent, explore the guide on [Creating Your First Agent](/agents/agents-kit-2/how-to/creating-agents/). It walks you through how to combine prompt engineering, function calling, and knowledge base integration—all within a Mendix app.
 
 ## Multi-Agent Systems
 
@@ -65,7 +65,7 @@ The system takes a user prompt as input, either entered directly or crafted usin
 
 Start from the [Agent Builder Starter App](https://marketplace.mendix.com/link/component/240369) from the Marketplace or add the [Agent Commons module](https://marketplace.mendix.com/link/component/240371) to your existing app and get started with agents and agentic patterns in Mendix.
 
-Read more about [Agent Commons](/agents/genai-for-mx/agent-commons/) in the GenAI reference guide.
+Read more about [Agent Commons](/agents/agents-kit-2/reference-guide/genai-for-mx/agent-commons/) in the GenAI reference guide.
 
 ### GenAI Showcase App