> For the complete documentation index, see [llms.txt](https://developer.emporix.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://developer.emporix.io/agentic-commerce-intelligence/agentic-intelligence/agents.md). # AI Agents Emporix Agentic AI introduces AI-powered agents as a new way of working, transforming ecommerce operations by enabling intelligent automation. These agents are designed to take on business processes, simplify operations, and unlock greater efficiency for merchants. Instead of relying on manual effort for repetitive or time-consuming tasks, involve these intelligent agents to react to events happening in the system and carry out some actions automatically. This approach not only saves time but also creates opportunities to scale business operations without adding complexity. There are two primary types of agents within the Emporix Agentic AI:
:books:Predefined agentsReady-to-use solutions provided by Emporix for common ecommerce needs. They are designed to run in the background, seamlessly integrating into business workflows. Emporix offers an AI Agents Library as an expandable catalogue of these prebuilt solutions to make their adoption easier./pages/G6wZGMKUUGW0uSOsfJKy
:robot-astromech:Custom agentsFor unique business requirements, merchants can use the Agentic AI to create and run custom agents. This offers full control over LLM choice, attached tools, and MCP-based capabilities – tailored to your processes, compliance requirements, and customer expectations.#custom-ai-agents
## Predefined agents Emporix provides ready-to-use agents for common ecommerce scenarios — for example, complaint handling, fraud detection, customer support, translations, and storefront guidance. Browse the [AI Agents Library](/agentic-commerce-intelligence/agentic-intelligence/agent-library.md) to see available templates and learn how to enable them in your tenant. When you create an agent from a library template, a read-only **Template Prompt** field shows the predefined workflow. You can use it as a reference when building your own custom agents. ## Custom AI Agents Example use cases where custom agents come in handy: * **Connecting to OpenAI** – Custom agents can be configured to use your own enterprise API token for GDPR-compliant production environments when connecting to market-leading language models like OpenAI. This allows for sophisticated AI interactions while maintaining compliance. * **Integrating with external business systems** – Agents can be granted access to external MCP servers to securely interact with systems like ERP (Enterprise Resource Planning), CRM (Customer Relationship Management), or other specialized services. This enables agents to: * Fetch product availability directly from an ERP system. * Process purchase orders by communicating with your ERP system. * **Managing credentials for third-party services** – Custom agents can manage credentials for various other third-party services or integrations, ensuring secure communication and automation across your entire digital landscape. Custom agents don’t have to work in isolation. The platform allows you to create multiple agents and connect them, enabling them to operate as a team. This teamwork approach is especially powerful when solving complex business goals that require multiple steps or domain-specific expertise. The example custom agents chain might look as follows: ```mermaid --- config: layout: fixed theme: base look: classic themeVariables: background: transparent lineColor: "#9CBBE3" arrowheadColor: "#9CBBE3" edgeLabelBackground: "#FFC128" edgeLabelTextColor: "#4C5359" --- flowchart LR subgraph subGraph0["CUSTOM AGENT CHAIN"] direction LR A["AGENT A

Fetch product data
from an ERP system"] B["AGENT B

Analyze data for
compliance or profitability"] C["AGENT C

Update the
ecommerce catalog"] end A -->|"hand over"| B B -->|"hand over"| C style A fill:#A1BDDC,stroke:#4C5359 style B fill:#DDE6EE,stroke:#4C5359 style C fill:#F2F6FA,stroke:#4C5359 classDef Class_02 stroke-width:1px,stroke-dasharray:0,stroke:#A1BDDC,fill:#DDE6EE class subGraph0 Class_02 style subGraph0 color:#4C5359 A@{ shape: rounded} B@{ shape: rounded} C@{ shape: rounded} ``` 1. One agent fetches product data from an ERP system. 2. A second agent analyzes that data for compliance or profitability. 3. A third agent updates the ecommerce catalog accordingly. Working together, the agents form a coordinated workflow that is more effective than any single agent working on its own. The Custom Agents feature makes Emporix not just a platform with AI capabilities, but a truly extensible agentic ecosystem. By combining predefined agents with custom-built ones, merchants can automate, adapt, and innovate with precision — building agent teams that reflect the way your business really works. ### Creating a custom agent **Prerequisites** Before starting the agent creation process, prepare what the agent is supposed to use: * [AI Tokens](/agentic-commerce-intelligence/agentic-intelligence/configuration/tokens.md) * [AI Tools](/agentic-commerce-intelligence/agentic-intelligence/configuration/tools.md) * [AI MCP](/agentic-commerce-intelligence/agentic-intelligence/configuration/custom-mcp.md) Follow these steps to create and configure a custom AI agent for specific tasks. {% stepper %} {% step %} #### Choose to create an agent In the Management Dashboard, go to the **Agentic AI** -> **AI Agents**. Choose **Add new agent** to start creating. Each field in the configuration is important to ensure that the agent behaves as expected and integrates seamlessly into your workflows. {% endstep %} {% step %} #### Provide agent details In the **General** tab, configure the agent's identity and capabilities: * **ID** – The unique identifier of the agent. This ID is critical, as it is used whenever the agent is triggered through an API endpoint. * **Agent Name** – A human-readable display name for the agent. * **Description** – A text field where you can describe the agent’s purpose and what tasks it is designed to handle. * **Tags** – Choose one or more tags that fit the agent's functionality for better searchability among other agents. * **Icon** – Select an icon associated with the agent for better visibility among other agents. * **User Prompt** – The core of your agent’s intelligence. Define how the agent should operate. {% hint style="success" %} **Prompt** A well-written and designed prompt guides the agent’s reasoning, behavior, and tone. We recommend the [prompt engineering best practices](/agentic-commerce-intelligence/agentic-intelligence/best-practices.md#prompt-engineering) to ensure your agent performs consistently and effectively. {% endhint %}
Creating custom agent

Creating custom agent

{% endstep %} {% step %} #### Configure AI model Each agent can be powered by a different language model (LLM). This gives you the flexibility to choose the best option for your specific use case. The **Model** tab includes configuration of the following options: * **Provider** – Choose the provider that powers the agent: * **Emporix OpenAI** – For testing purposes only. This option lets you get started quickly without creating an external account.

The Emporix OpenAI must not be used for production. For production, use your own enterprise license or custom model to remain GDPR compliant. Note that there is a limited number of tokens available per tenant: * input tokens: 2 000 000 * output tokens: 500 000

* **Anthropic** * **Google** * **OpenAI** * **Self-hosted Ollama** * **Self-hosted vLLM** * **Token** – Select a previously configured token (from the [AI Tokens](/agentic-commerce-intelligence/agentic-intelligence/configuration/tokens.md) view) to be used for communication with the LLM provider. * **Models** – Each LLM provider offers multiple models (for instance, OpenAI GPT-4, Claude Sonnet 4.5, Gemini 2.5 Flash, and more). Specify the model you want to use with your agent. You can choose between **Standard** models offered by the LLM providers or add a **Custom** model identifier as supported by your provider. * **Max Tokens** – Set the maximum length of responses the agent can generate, based on the particular model's specification and your use case. * **Enable Memory** – Toggle the memory option on if you want the agent to keep the conversational data within the session. It is useful for chatbots or collaboration agents that need to remember previous queries and answers to be able to make logical references and adequate data matching. The memory is kept within one `session-id`. * **Temperature** – Define the level of randomness and creativity of your agent. Values range from `0.0` to `1.0`. * Lower values (for example, `0.1`) → More predictable, rule-following outputs. * Higher values (for example, `0.8`) → More creative, useful for tasks like product description generation. * **Recursion Limit** – Define how many iterations the agent is allowed to perform before it stops the execution. This parameter prevents infinite loops and ensures safety and stability of the agent.
Configuring LLM model for custom agent

Configuring LLM model for custom agent

{% endstep %} {% step %} #### Define activation rules In the **Trigger & Constraints** tab, define when and how the agent is triggered. * **Required scopes** – Optional. Restrict which users or integrations can trigger the agent by selecting one or more scopes. If you leave this empty, no scope-based restriction is applied. * Anonymous – Anyone can trigger the agent; no scope is required. * Customer – A user requires the `ai-execution_manage_own` scope to invoke the agent. * Employee – A user requires the `ai-execution_manage` scope to invoke the agent. * Integration – An external integration, such as an ERP or CRM system, requires the `ai-execution_manage` scope to trigger the agent. * **Trigger Type** – Define how the agent is activated: * **API** – Call the Emporix endpoints to trigger the agent. This is useful when you want to start an agent with specific content or data passed through an API invocation. You can also use this option to include the agent in a [Value Stream](/value-stream-modeller/value-streams/value-stream-introduction.md). * **Commerce Event** – Make the agent listen to specific commerce-related events happening in the system (for example, `order-created`) and act immediately when those events occur. Select the ones to act upon from the available commerce events list. * **Slack** – This option is available only when creating or configuring a [Support Agent](/agentic-commerce-intelligence/agentic-intelligence/agent-library/support-agent.md) instance from the library, not for custom agents created from scratch.
Configuring trigger for custom agent

Configuring trigger for custom agent

{% endstep %} {% step %} #### Optional: Define constraints for commerce event triggers Complete this step only when **Commerce Event** is selected as the trigger type in the previous step. The Commerce Event trigger type allows you to define specific rules for when the agent should act. You can constrain the agent to specific conditions occurring in the system. * You can combine several conditions using `AND` or `OR` operators. * Add a condition manually by specifying a relevant payload path and its value. Select the appropriate operator to define the value. The conditions you build are immediately reflected in the JSON output. * For your convenience, you can choose **Generate Condition** – this option helps you enable the [Agentic Filters Creator Assistant](/agentic-commerce-intelligence/agentic-intelligence/agent-library/filters-agent.md). Prompt the agent in natural language to create a set of conditions for you. If the conditions are more complex, only the JSON output is visible.
Configuring trigger constraints for custom agent

Configuring trigger constraints for custom agent

JSON representation of complex constraints for custom agent

JSON representation of complex constraints for custom agent

Review the conditions, modify the JSON directly if needed, and choose **Apply** to save the constraints. {% endstep %} {% step %} #### Attach tools The agent can be equipped with additional tools it can use in the background to retrieve and process information, or to access different resources within or outside the system. In the **Tools** tab, select the tools required to enable the agent to execute its tasks: * Select built-in domain tools (for example, product, order, or customer). For more information about Emporix MCP domains, see the [Emporix MCP Server](/agentic-commerce-intelligence/mcp-in-emporix/mcp.md#available-tools). * **Native Tools** – Extend the agent with additional capabilities, such as Slack integration or RAG capabilities. For example, the agent can use the Slack tool to collaborate with humans in context-aware Slack channels. Prepare tools upfront in the [AI Tools](/agentic-commerce-intelligence/agentic-intelligence/configuration/tools.md) view. * **Custom MCP** – Add external servers configured by yourself (for example, ERP or CRM). Define custom MCP in the [AI MCP](/agentic-commerce-intelligence/agentic-intelligence/configuration/custom-mcp.md) view before you attach them to your agent.
Attaching AI tools to custom agent

Attaching AI tools to custom agent

{% endstep %} {% step %} #### Configure agent collaboration The agent might need assistance from other agents to correctly and smoothly process information, or to hand over its task to another agent when its job is complete. Connect one or multiple agents together, allowing them to operate as a team. In the **Prompt**, define when and how the agent hands over an action to another agent. You can attach as many agents as you need to create multi-step, collaborative workflows.
Connecting collaboration agents

Connecting collaboration agents

{% endstep %} {% step %} #### Save the agent Once ready, confirm your agent definition with **Save**. As a result, the agent is available on the **My Agents** list. {% endstep %} {% endstepper %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://developer.emporix.io/agentic-commerce-intelligence/agentic-intelligence/agents.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.