Agent builder concepts and refguide

content/en/docs/appstore/use-content/platform-supported-content/modules/genai/_index.md

@@ -50,7 +50,10 @@ To help you get started, the following sections list the available GenAI compone
 | [GenAI Showcase App](https://marketplace.mendix.com/link/component/220475) | Understand what you can build with generative AI. Understand how to implement the Mendix Cloud GenAI, OpenAI, and Amazon Bedrock connectors and how to integrate them with the Conversational UI module. |Showcase App | 10.21 |
 | [RFP Assistant Starter App / Questionnaire Assistant Starter App](https://marketplace.mendix.com/link/component/235917) | The RFP Assistant Starter App and the Questionnaire Assistant Starter App leverage historical RFPs (or question-answer pairs) and a continuously updated knowledge base to generate and assist in editing responses to RFPs, offering a time-saving alternative to manually finding similar responses and enhancing the knowledge management process.  | Starter App | 10.21 |
 | [Support Assistant Starter App](https://marketplace.mendix.com/link/component/231035) | Learn how to combine common GenAI patterns, such as function calling and RAG to build your support assistant. Connect it to a model like Anthropic Claude via Mendix Cloud GenAI or Amazon Bedrock or use an (Azure) OpenAI subscription.  | Starter App | 10.21 |
+| [Agent Builder Starter App](https://marketplace.mendix.com/link/component/240369) | See an example of how to build an agentic mendix application. Use the Agent Builder from Agent Commons to build your support assistant. | Starter App | 10.21 |
 | [GenAI Commons](/appstore/modules/genai/commons/) | Common capabilities that allow all GenAI connectors to be integrated with the other modules. You can also implement your own connector based on this. | Common Module | 10.21 |
+| [Agent Commons](/appstore/modules/genai/genai-for-mx/agent-commons/) | Build agentic functionality using common patterns in your application by defining, testing, and evaluating agents at runtime. | Common Module | 10.21 |
+| [MCP Server](https://marketplace.mendix.com/link/component/240380) | Make your Mendix business logic available to any agent in your enterprise landscape with the Mendix MCP Server module. Expose reusable prompts including the ability to use prompt parameters. List and run actions implemented in the application as tool. | Common Module | 10.21 |
 | [Conversational UI](/appstore/modules/genai/conversational-ui/) | Create a Conversational UI, manage prompts or monitor token consumption in your app. | UI Module | 10.21 |
 | [Mendix Cloud GenAI Connector](/appstore/modules/genai/mx-cloud-genai/MxGenAI-connector/) | Connect to Mendix Cloud and utilize Mendix Cloud GenAI resource packs directly within your Mendix application. | Connector Module | 10.21 |
 | [OpenAI Connector](/appstore/modules/genai/openai/) | Connect to (Azure) OpenAI. | Connector Module | 10.21 |

content/en/docs/appstore/use-content/platform-supported-content/modules/genai/concepts/agents.md

@@ -0,0 +1,70 @@
+---
+title: "GenAI Agents"
+url: /appstore/modules/genai/agents/
+linktitle: "GenAI Agents"
+weight: 40
+description: "Describes Agents and Agentic Patterns as used with generative AI in Mendix"
+---
+
+## Introduction
+
+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 [building your first agent in Mendix](/appstore/modules/genai/how-to/howto-single-agent/). It walks you through how to combine prompt engineering, function calling, and knowledge base integration—all within a Mendix app.
+
+## Multi-Agent systems
+
+Sometimes, a single agent is not enough for more complex use cases. In such cases, a multi-agent solution is needed. Multi-agent architectures go beyond single-agent implementations when tasks become too complex for one agent to handle alone. While single agents work well for simple, well-defined tasks, more complex or uncertain scenarios require multiple agents to collaborate. Multi-agent systems enable the coordination of business processes, specialized task allocation, and protocol execution by invoking dedicated sub-agents, often dynamically. This approach leads to better performance and more efficient operations compared to relying on a single agent to handle everything.
+
+## Pattern Overview
+
+When building agents, choose a pattern that aligns with your system's goals. Ensure that task allocation and coordination work as intended and lead to the desired outcomes. You will find examples of common patterns below. For practical implementations, check out the GenAI Showcase App.
+
+### Prompt Chaining
+
+This approach uses a linear chain of multiple LLM calls, where the output of one call becomes the input for the next. The output can be passed directly or included in the next prompt with additional instructions. Each LLM call has its own system prompt and represents a distinct step in a larger process with an overarching goal. You do not need to use the same model for every step. The model can be selected based on the task of each LLM step.
+
+The system takes a user prompt as input, either typed directly or generated using prompt engineering techniques. Its output is typically the plain response from the final LLM call in the chain.
+
+ {{< figure src="/attachments/appstore/platform-supported-content/modules/genai/agents/Linear-Chaining.svg" >}}
+
+### Prompt Chaining with Gatekeeper
+
+This is an extension of the linear chain of multiple LLM calls. Now, the gatekeeper LLM call is part of the linear flow. Unlike other steps, the gatekeeper does not always pass its output directly to the next call. Its role is to assess the input and decide whether to continue the flow or break out, typically in "unhappy" or exception scenarios. If the gatekeeper determines that the process should proceed, the next LLM call receives the same input that the gatekeeper received.
+
+As with the previous pattern, the system takes a user prompt as input, either entered directly or generated through prompt engineering techniques. The output is typically the plain result of the final LLM call in the happy flow. In an unhappy scenario, developers can choose to return either the gatekeeper agent’s response or a predefined static message.
+
+ {{< figure src="/attachments/appstore/platform-supported-content/modules/genai/agents/Linear-Chaining-Gatekeeper.svg" >}}
+
+### Evaluator-Optimizer
+
+In the evaluator-optimizer workflow, one LLM generates a text, and another evaluates it by providing feedback. This loop continues until the output meets the evaluation criteria or reaches a maximum number of attempts.
+
+Alternative names for this pattern are:
+
+* LLM-as-a-judge (also used in testing or evaluation frameworks, so context is important to avoid confusion)
+* Generator evaluator
+
+The input of this system is a user prompt, either typed directly by the user or constructed using prompt engineering techniques. The output of the system is the plain output of the last iteration of the Generator Agent LLM call, as approved by the Evaluator Agent.
+
+ {{< figure src="/attachments/appstore/platform-supported-content/modules/genai/agents/Evaluator-optimizer.svg" >}}
+
+### Routing
+
+This pattern is especially effective when the system needs to handle a variety of specific tasks. For each task, a dedicated agent is created with a clear focus on its assigned responsibility. When the system is triggered, a router agent classifies the input and determines which supported task most closely matches the user's intent. Once a match is found, the original input (which may include chat history) is passed to the appropriate agent. This process is often referred to as “hand-off”. It transfers full responsibility to the selected agent, which processes the input and generates an output, typically without any awareness of the router's involvement.
+
+The system takes a user prompt as input, either entered directly or crafted using prompt engineering techniques. The output is typically the plain response from the agent chosen by the Router Agent. In some variations, the Router Agent may choose not to hand off the input if it determines that the request falls outside the system's supported scope. In such cases, the system returns either the Router Agent's own response or a static message explaining why the request could not be processed.
+
+ {{< figure src="/attachments/appstore/platform-supported-content/modules/genai/agents/Routing.svg" >}}
+
+## Learn More
+
+### Agent Builder
+
+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](/appstore/modules/genai/genai-for-mx/agent-commons/) in the GenAI reference guide.
+
+### Additional Information
+
+ Read the blog post on [Multi-agent systems in a Mendix app](https://www.mendix.com/blog/how-multi-agent-ai-systems-in-mendix-can-train-you-for-a-marathon/)