AI Agent block
This article explains how to configure and use the AI Agent block in Autify Genesis. The AI Agent block is the basic workflow unit that uses an LLM (large language model) to generate text or structured data.
Basic settings
Click a block to open the Details tab in the inspector on the right. Configure the following fields.
Name
Display name of the block. Appears on the canvas and in the inspector.
Description
Additional explanation of the block's role or processing.
Prompt
Instructions for the AI agent. Reference user inputs or explicitly defined inputs as {{variable_name}}. Outputs from connected upstream blocks are passed automatically as context.
Save output as artifact
When enabled, the output of this block is saved as a downloadable artifact.
Review required
When enabled, the workflow pauses after this block runs and waits for manual approval.
Model
Select the AI model to use. Multiple models are available per provider.
Writing prompts
There are two ways to use values in a prompt: reference user inputs with {{variable_name}}, or use outputs from connected upstream blocks as automatic context.
Referencing user inputs as variables
Use {{variable_name}} in a prompt to reference user inputs provided at run time or inputs explicitly defined on this block.
To insert a variable, click Variable at the top of the prompt editor to open the variable insertion menu. Depending on your configuration, the menu shows the following types.
Inputs
Input variables defined on this block
{{input_key}}
Context
User inputs provided by the user at workflow run time
{{my_input}}
Block outputs
Outputs from other blocks added explicitly as inputs
{{previous_block}}
If you only want to use upstream block outputs as automatic context, you do not need to insert a variable.
If a variable referenced in the prompt is not defined in the block's inputs, validation fails. Use the variable insertion menu to pick from the available variables.
Referencing outputs from connected upstream blocks
Outputs from upstream blocks connected to this block on the canvas are passed automatically as context at run time. To use upstream outputs, you do not need to insert {{variable_name}} each time.
When there are connections, the count of available connected blocks and the block names appear below the prompt field. Write your prompt assuming the outputs of those blocks.
Referencing files, folders, and skills
You can insert workspace files, folders, and skills directly into the prompt so the AI agent can use them during execution.
Files and folders
Click Add file at the top of the prompt editor to open the file browser.
Search for files, browse folders, and select the files or folders you want to insert.
Add the selected items. They are inserted into the prompt as references.
To reference a particular file version, type
@in the prompt, open the menu next to the file name, and select the target version.
When you reference a folder, the AI agent can inspect files inside that folder during execution.
Project file and folder references are resolved again at run time from their internal IDs. If you move or rename a referenced item after writing the prompt, Genesis updates the current /project/files/... path automatically when the workflow runs.
AI agents can also create new project files or edit existing text files directly during chat and workflow runs. Creating or editing project files supports Markdown, text, JSON, and HTML files. If an agent creates a file in the sandbox first, it can save that file back into the project file explorer before the run ends.
After the run finishes, files written by the agent are shown in the chat header or the workflow run details so you can open them directly.
Skills
Skills are reusable instruction packs discovered from SKILL.md files uploaded to the workspace or included in connected codebases.
Click Add skill at the top of the prompt editor, or type
/at the start of a new token in the prompt.Search for the skill you want to use.
Select the skill from the list.
The skill is inserted into the prompt as
/skill-name.
If the workspace skill mode is Automatic, the AI agent can also choose relevant skills on its own. Referencing /skill-name always applies that skill to this prompt.
Refining a prompt
Click Refine in the top-right of the prompt editor to use the AI-based prompt improvement feature.
Auto improve
Tightens language and clarifies steps
Make actionable
Rewrites the prompt to include clear outputs, acceptance criteria, and constraints
Custom instruction
Enter your own improvement request and the AI adjusts the prompt accordingly
Selecting a model
From the Model dropdown, select a model that fits the task. For details on available models, see AI Models.
Saving as an artifact
When you enable Save output as artifact, this block's output is saved as a downloadable file.
Turn on Save output as artifact in the inspector.
Enter a file name in Artifact title (if omitted, a name is generated automatically).
In Artifact format, choose Markdown, JSON, or HTML.
In On re-run, choose how the artifact should be updated on re-runs.
ModeDescriptionRegenerate
Each run writes a new artifact from scratch. Good when every run should stand on its own.
Edit in place
Re-runs apply targeted edits to the latest version of the existing artifact.
If Structured output is enabled, the artifact format is fixed to JSON.
Saved artifacts can be accessed and downloaded from the execution details screen after the workflow run completes. Saved HTML artifacts open as rendered previews from execution details and file details. For versions created with Edit in place, open the diff panel against the previous version from the Edits chip, which shows the added and removed line counts. Click a line in the diff panel to jump to the matching position in the current document.
Using the review feature
When you enable Review required, the workflow pauses after this block runs and waits for human review. For details, see Review feature in "Create and Edit Workflows."
Advanced options
Expand Advanced options in the inspector to access the following advanced settings.
Structured output
When you enable Structured output, you can define the AI agent's output format with a JSON Schema. This makes it easier for downstream blocks to access specific fields in this block's output.
You can define a schema in two ways.
Schema editor
UI designer mode to add, delete, and configure fields one by one
JSON
Text mode to input a JSON Schema directly
In the schema editor, configure the following for each field.
Field name
Property name in the output JSON
Schema type
Select from string / number / boolean / array / object
Description (optional)
Additional explanation for the AI agent. Helps the agent generate appropriate values.
Required
Whether the field is required
Click Refine schema to enter natural-language instructions for the AI to adjust the schema automatically.
When structured output is enabled, validation fails if the schema has no fields.
Thinking effort
For supported reasoning models, Thinking effort lets you adjust how much internal reasoning the AI Agent block uses. Leave it on Default to use the model's recommended setting. Raise it for more complex tasks, or lower it when you want faster responses for simpler tasks.
If the selected model does not support Thinking effort, the control is unavailable.
Tools
Under Tools, select the built-in tools the AI agent can use during execution. Enabled tools are called automatically when the agent determines they are needed.
Enabled tools appear as icons on the block on the canvas. Click the + button in the bottom-right of the block to add or remove tools in a popover with the same content as the Tools tab, without opening the inspector.
When external service integrations such as GitHub, Slack, or Aximo are configured, the corresponding tools do not appear in the Tools tab, and the AI agent uses them automatically at run time. For the full list of tool types and conditions, see Tools.
MCP server
Under MCP servers, you can add external MCP (Model Context Protocol) connections registered on the workspace to extend the agent's capabilities. For how to register external MCP connections, see Workspace Settings.
Troubleshooting
Model errors
"The selected model '{model}' is unavailable"
The selected model is not provided by any enabled provider
Select a different model
Structured output errors
Cannot run with structured output enabled
No fields are defined in the schema
Add fields in the schema editor, or disable structured output
"Invalid JSON schema"
The content entered in JSON mode is not a valid JSON Schema
Verify and fix the JSON syntax
Prompt errors
"Variables used in the prompt are not defined in inputs"
An input corresponding to a {{variable_name}} in the prompt is missing
Reinsert the variable from the variable insertion menu, or add the matching input in the Inputs tab
The block shows a file validation error before execution
A file referenced in the prompt was deleted from the workspace
Remove the stale reference, or insert the current file again from Add file, then rerun the workflow
MCP server errors
"Using an MCP connection that does not exist in this workspace"
The MCP connection referenced by the block was deleted, or belongs to another workspace
Re-add the connection in MCP server settings
The connection check shows "Error"
The MCP server is not responding, or the credentials are invalid
Verify the URL and credentials, then edit the connection
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