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Copy file name to clipboardExpand all lines: content/en/docs/marketplace/genai/how-to/create-single-agent.md
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@@ -23,10 +23,11 @@ The type of agent you can build is a single-turn agent, which means that:
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* It focuses on specific task completion.
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* It uses a knowledge base and function calling to retrieve data or perform actions.
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This document covers two approaches to defining an agent for your Mendix app. Both approaches leverage the capabilities of Mendix' Agents Kit:
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This document covers three approaches to defining an agent for your Mendix app. Both approaches leverage the capabilities of Mendix' Agents Kit:
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* The first approach uses the [Agent Builder UI to define agents](#define-agent-commons) at runtime by the principles of Agent Commons. It enables versioning, development iteration and refinement at runtime, separate from the traditional app logic development cycle.
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* The second approach [defines the agent programmatically](#define-genai-commons) using the building blocks of GenAI Commons, and is more useful for very specific use cases and when the agent needs to be part of the code repository of the app.
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* The first approach uses the [Agent Editor in Studio Pro](#define-agent-editor). It is used for creating and iterating on agent definitions as part of the app model, leveraging existing development capabilities of the platform to define, manage and deploy agents as part of a Mendix app.
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* The second approach uses the [Agent Builder UI to define agents](#define-agent-commons) at runtime by the principles of Agent Commons. It enables versioning, development iteration and refinement at runtime, separate from the traditional app logic development cycle.
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* The third approach [defines the agent programmatically](#define-genai-commons) using the building blocks of GenAI Commons. It is more useful for very specific use cases, especially when the agent needs to be part of the code repository of the app.
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### Prerequisites {#prerequisites}
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This method provides greater flexibility in managing and sharing functions across different applications and environments.
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## Define the Agent Using the Agent Editor {#define-agent-editor}
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The main approach to create and manage agents makes use of the [Agent Editor](https://marketplace.mendix.com/link/component/257918) in the Studio Pro. This extension allows you to manage the lifecycle of your agents as part of the app model. You can define Agents as documents of type "Agent" in your app while working in Studio Pro, alongside related documents such as Large Language Models, Knowledge bases, and Consumed MCP services.
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{{% alert color="warning" %}}
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The Agent Editor will become available shortly after the Mendix Studio Pro 11.9 release as a downloadable extension on the Mendix Marketplace. Click **Add to Saved** on the [Marketplace listing](https://marketplace.mendix.com/link/component/257918) and stay tuned for updates!
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## Define the Agent Using Agent Commons {#define-agent-commons}
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The main approach to set up the agent and build logic to generate responses is based on the logic part of the Agent Commons module. Start by defining an agent with a prompt at runtime, then, through the same UI, add tools, (microflows as functions) and knowledge bases to the agent version.
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An alternative approach to set up the agent and build logic to generate responses is based on the logic part of the Agent Commons module. Start by defining an agent with a prompt at runtime, then, through the same UI, add tools, (microflows as functions) and knowledge bases to the agent version.
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### Set up the Agent with a Prompt
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## Define the Agent Using Microflows {#define-genai-commons}
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This is an alternative approach to the steps described in the [Define the Agent Using Agent Commons](#define-agent-commons) section. Find out how to set up the agent and build logic to generate responses, using microflows for empowering the agent. You start with a prompt at runtime, and add functions and knowledge bases to the microflow at design time.
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This is an additional approach as alternative to the steps described in previous sections. Find out how to set up the agent and build logic to generate responses, using microflows for empowering the agent. You start with a prompt at runtime, and add functions and knowledge bases to the microflow at design time.
description: "Describes the purpose, configuration, and usage of the Agents Editor and the Agent Editor Commons modules from the Mendix Marketplace that allows developers to build, define, and refine Agents, to integrate GenAI principles, and Agentic patterns into their Mendix app."
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---
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## Introduction
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{{% alert color="warning" %}}
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The Agent Editor will become available shortly after the Mendix Studio Pro 11.9 release as a downloadable extension on the Mendix Marketplace. Click **Add to Saved** on the [Marketplace listing](https://marketplace.mendix.com/link/component/257918) and stay tuned for updates!
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{{% /alert %}}
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The [Agent Editor](https://marketplace.mendix.com/link/component/257918) module enables users to develop, test, and optimize their GenAI use cases by creating effective agents that interact with large language models (LLMs). With the Agent Editor module, you can define Agents at design time in the Studio Pro (11.9.0 and above), and manage their lifecycle as part of your project by taking advantage of existing platform capabilities such as model documents, version control, and deployment capabilities. You can define and develop Agents locally and consecutively deploy them to cloud environments directly with the app model.
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The Agent Editor is compatible with the Agent Commons module. Using this module, you can define and manage prompts, microflows (as tools), external MCP servers, knowledge bases, and large language models to build agentic patterns that support your business logic. Additionally, it allows you to define variables that act as placeholders for data from the app session context, which are replaced with actual values when the end user interacts with the app.
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The Agent Editor module includes a Studio Pro extension that allows users to define GenAI Agents as documents in the app model. The Agent Editor Commons module, which is installed as part of the same package, includes logic and activities to call these agents from microflows in a running application.
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