AI Agent
Description
Runs an LLM agent using the task prompt and available tools, returning either text or structured output. The component is available after at least one AI integration is configured in the Admin Panel.
Parameters and configuration
| Name | Description |
|---|---|
| LLM Integration | LLM integration to use. |
| Chat model name | The chat model to use. The available models depend on the LLM Integration selected. |
| Prompt | Description of the concrete task to be performed. |
| MCP tools access | You can choose here which tools exposed by the defined MCP integrations can be used. |
| Output variable name | Variable name under which node result will be available in a subsequent nodes. |
Advanced parameters:
| Name | Description |
|---|---|
| System message | Reusable rules that define how the agent should behave. Some AI agents providers use 'agent instructions' term here. |
| Max iterations | Maximum reasoning/tool-use loop count. |
| Structured output | Optional; json schema of the desired response. |
| Temperature | Temperature is a hyperparameter that controls the randomness of text generation. Lower values (e.g., 0.2) make the model’s output more focused and deterministic by favoring the most likely tokens, while higher values (e.g., 0.8 or above) produce more diverse and creative responses by flattening the probability distribution. |
Returned value
This component returns response as a JSON object or text.
Additional considerations
- You can use string template during prompt creation to use data available in the scenario (e.g., documents retrieved from the vector store)
- Currently only selected models are available. Contact us if you need to use additional ones.