DigiFi AI Agent

Overview of the DigiFi AI Agent

The DigiFi platform features a built-in AI assistant that helps your team streamline the review and completion of application tasks using generative AI.

Once enabled, the AI Agent becomes available within application tasks, allowing you to review documents, update data, and complete tasks more efficiently. It can perform actions such as reading and verifying documents, extracting and updating variables, assigning labels, performing calculations, and adding comments.

Enabling the AI Agent

The AI Agent can be found in the DigiFi Marketplace under the AI Tools category. Simply enable the toggle to activate the agent.

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Activating the AI Agent may incur additional costs. Each AI action, such as updating labels or data, and each document analysis may generate charges. Please refer to your contract for details.

AI Agent Availability and Permissions

The AI Agent is available within application tasksand can also be triggered automatically through Automation Builder.

The Agent operates with the same permissions as the user working with it. For example, if a user does not have access to certain data, the Agent cannot access it either.
Multiple users can collaborate with the Agent on the same tasks.

AI Agent Supported Document Types

Supported file formats include: .pdf, .json, .csv, .txt, .xml, .svg, .png, .jpg, .jpeg, .gif, .webp

How the AI Agent Works

When working with a task, the AI Agent goes through three phases:

Planning – Creates a plan based on your internal instructions.
Processing – Reviews documents, analyzes data, and performs updates.
Completion – Summarizes the work done and records the results.

The Agent begins by reading your internal instructions and generating a structured plan. These steps can include actions such as:

Reading task data (title, internal instructions, variables in the data tab, documents, labels, status)
Updating task data (labels, status, variables)
Reading and analyzing documents (verifying authenticity, extracting data, matching with existing data)
Adding task comments (and reading existing ones)
Reading some application data (status name, application labels, application variables and IDs)
Performing calculations based on available information and updating variables

During Processing, the Agent executes the plan step by step and logs each action. For example, when analyzing a document, it displays the document name and ID and provides verification results if requested.

Every update completed by the agent is marked with a special icon in the application history, making it easy to track.

If the history record shows API, the action was executed automatically (via workflow automation).
If it shows a user name, the action was performed manually in guided mode.

AI Agent Execution Modes

The AI Agent supports two execution modes:

Guided Mode (Default)

In guided mode, certain actions require user confirmation before being applied. Users can confirm or decline suggested updates and provide feedback if adjustments are needed.

The Agent may pause and request guidance if instructions are unclear, critical data is missing, or business judgment is required.

Automatic Mode (Workflow Automation)

Users can enable the “Run Automatically” toggle to allow the AI Agent to execute all internal instructions within a task without user confirmation.

In automatic mode:

The Agent executes defined tasks without step-by-step confirmations.
Confirmation-related blocks are hidden during execution.
All actions are logged.
A complete execution history is available after processing.

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To automate AI Agent in your workflow tasks you can use the “Run AI Agent” action in Automation Builder.
Place Run AI Agent actions at the end of your workflow.
The AI Agent runs longer than other automated actions and may take 1-2+ minutes to complete.
If subsequent workflow actions depend on its output, add a Time Delay to ensure processing is complete before the workflow continues.

Best Practices for Writing AI Agent Instructions

To get the most out of the AI Agent, follow these guidelines when writing instructions:

Clear Instructions: Give direct instructions with clear rules. The AI Agent needs to understand exactly what you want it to do, with precise guidelines.
Specify Formats: If the agent needs to save a value, clearly state the format (e.g., MM/DD/YY) to ensure compatibility with the variable.
Use Full Variable Names: When extracting data, specify the variables where the data should be saved.
Separate Steps: Use multiple, clearly separated steps (e.g. 1, 2, 3) to provide the agent ordered, multi-step instructions.
Use Conditional Instructions: Create “if-then” rules within your text where needed. For example: if the document seems suspicious, assign a label and update a variable.
Detail Calculations: When instructing the agent to perform calculations, specify which values to use and the formula. For example: “If pay frequency and gross pay are available, use: gross pay × frequency multiplier (52, 26, 24, or 12).”

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❌ Bad: “Check the ID.” ✅ Good: “Complete the following steps to help verify the applicant's identity:

Validate the identity document to determine if it appears to be either a passport or driver's license, authentic, not manipulated and not expired. If the document is the wrong type, or appears to be fake, manipulated or expired, assign the task label "Invalid Document", provide an explanation in the Task Comments and update the Task's status to "Failed".

Compare the first name, last name, address and date of birth in the identity document to the values in the variables Borrower First Name, Borrower Last Name, Borrower Home Address and Borrower Date of Birth. If they are not the same or very similar, assign the task label "Data Mismatch", provide an explanation in the Task Comments and update the Task's status to "Failed".

If all steps are completed without issues, change this Task's status to "Done""

Important: Handling Missing Documents or Data

If the AI Agent cannot find required inputs (such as document or label referenced in the instructions), it will pause execution and request additional input – in both guided and automated runs.

When triggered via Automation Builder, the Agent may stop processing and wait for the missing information before continuing.

Please note:

If a document referenced in the instructions is missing, the Agent will request it.
If a label does not exist, it cannot be assigned.
If a variable is not linked to the task, it cannot be updated.

Behavior depends on how the instructions are written. If the instructions allow skipping missing data, the Agent will proceed accordingly.

Clearly define how to handle missing documents, labels, or variables in your task instructions to avoid interruptions.