Workflows Studio now features a redesigned homepage powered by AI. Instead of manually configuring every node, form, and connection from scratch, you can describe the workflow you want in plain English — and AI builds it for you.

The AI-generated Workflows homepage replaces the traditional app listing as the default landing page in Workflows Studio. When you open Workflows Studio, you're greeted with a prompt-driven interface where you can:

Describe your workflow requirements in natural language
Attach reference documents for additional context
Review and approve an AI-generated plan before any technical build begins
Watch the workflow get built on a side-by-side canvas that refreshes in real time as the AI works
Let AI generate a complete workflow app — with forms, nodes, and logic — in seconds

You can still create apps manually using the app listing page within Workflows Studio.

Examples

Action workflow

Workflow with a cURL

Approval workflow

How It Works

Step 1: Describe Your Workflow

On the Workflows Studio homepage, you'll see a text input with the prompt: "Describe the workflow you want to create."

Type a natural-language description of the process you want to automate. Be as specific as possible — include details about:

What data needs to be collected (form fields)
Who should approve or review
What actions should happen after submission
Any conditions or branching logic

Example prompts:

You can be as detailed or as brief as you like. The AI adapts — detailed prompts give you a more precise first draft, while simple prompts let the AI infer the structure from action configurations and best practices.

Detailed prompts:

"Create a leave request workflow where employees fill in leave type, start date, end date, and reason. The request should go to their manager for approval. If approved, update the leave balance. If rejected, notify the employee."
"Build an expense reimbursement form that collects expense category, amount, receipt upload, and description. Route to finance for approval if amount exceeds 5000, otherwise auto-approve."

Simple prompts — naming the application lets the AI identify required fields from the integration's configuration:

"Create a leave request workflow using Workday with manager approval"
"Expense reimbursement via SAP SuccessFactors with finance approval"
"Raise an IT ticket in ServiceNow for new joiners"
"Employee onboarding checklist with tasks in BambooHR"

Step 2: Attach Context (Optional)

You can attach up to 10 files to provide additional context — such as existing process documents, policy PDFs, or reference screenshots. The AI uses these to better understand your requirements and generate a more accurate workflow.

Step 3: View & Approve the Plan

Before any nodes, forms, or connections are actually created, the AI first produces a structured plan for your review. This is a deliberate checkpoint — the workflow is not built until you explicitly approve the plan. This prevents the AI from spending effort building something that doesn't match what you had in mind, and gives you a chance to course-correct cheaply.

What the plan contains

The plan is presented in the chat as a structured proposal with the following sections:

Nodes — A list of every step the AI intends to create, with each node's type (Input, Approval, Action, Decision, Function, Mapper, Update, End, etc.), a short name, and what that step does.
Flow — How the nodes connect end-to-end, starting from the Trigger and the initial input, including any branches, merges, or conditional paths.
Forms — Which forms are attached to which nodes, and the list of fields each form collects.
Trigger & Audience — Who can initiate the workflow (e.g., specific roles or groups), how it can be triggered (Manual / API / Webhook), and any audience restrictions.
Error Handling — What happens if a critical step fails or gets rejected — for example, fallback paths, retries, or notifications.
Additional Notes — Other design decisions such as whether a Sync Block is used, timeout values, and notification requirements.

Naming your workflow during planning

While reviewing the plan, the AI will also ask whether you have a preferred name and identifier for the workflow, or whether it should auto-generate them.

If you provide a name (e.g., "Call it Travel Reimbursement Request"), the AI uses your name as the first candidate when creating the app.
If you don't provide one, the AI generates a set of candidate names derived from your prompt and picks the first available one.
You can change the name later from the app settings, but providing it here saves a step.

Requesting changes vs. approving

The plan is iterative — you can refine it as many times as you need before approving:

Request changes by replying in chat — e.g., "Add a step to notify HR after approval", "Make the amount field required", "Change the approver to the skip-level manager", or "Remove the decision node, route everything through finance". The AI re-presents the full updated plan (not just the diff) after every change so you always see the latest version in one place.
Approve the plan by sending a clear confirmation in chat — e.g., "Looks good", "Go ahead", "Build it", or "Approved". Only after this confirmation does the AI start creating the actual app.

Tip: Review the Approvers, Trigger & Audience, and Error Handling sections carefully — these are the parts that are most expensive to fix later, since they affect routing and visibility.

Step 4: AI Generates the App

Once you approve the plan, the AI creates the app and begins building. The interface switches into a side-by-side view:

Left panel — Chat: The AI continues to communicate progress in the chat, showing "thinking" indicators as different specialist agents (Workflow Designer, Form Builder, Node Configurator) take turns building parts of the workflow.
Right panel — Live workflow canvas: The Flow Builder canvas opens alongside the chat and refreshes automatically every time the AI commits a change — when a node is added, an edge is drawn, a form is generated, or an action is configured, you see it appear on the canvas within seconds.

You don't need to click refresh — the canvas listens for updates from the AI in real time and re-renders as the workflow takes shape. You can keep chatting on the left while watching the structure grow on the right.

Under the hood, this is what happens during build:

A new workflow app is created automatically with the name and identifier you confirmed in the plan (or an AI-generated one if you didn't specify).
AI generates the workflow structure — trigger nodes, input forms, approval steps, action nodes, decision logic, branches, merges, and end states are added to the canvas one phase at a time.
Forms are built and attached to the relevant nodes.
Action nodes are configured end-to-end, including connection selection, action discovery, field mapping, and auto-test (see AI-Assisted Action Node Configuration below).

Step 5: Review and Refine

After the initial generation completes, you can:

Continue chatting with the AI to modify the workflow — add nodes, change form fields, adjust approval logic, or update conditions. The canvas on the right will continue to refresh with each change.
Switch to the Flow Builder to visually inspect and fine-tune the generated nodes and edges on the canvas directly.
Edit forms in the Form Builder to customize field types, validations, and layout.

Once satisfied, publish the app to make it available to employees.

What AI Generates

When you describe a workflow, the AI creates a fully functional draft app that includes:

Forms

Structured input forms with relevant field types — text inputs, dropdowns, date pickers, file uploads, checkboxes, and more — based on the data you described.

You don't always need to specify every form field explicitly. When your prompt names a specific application (e.g., "raise a ticket in ServiceNow" or "submit leave in Workday"), the AI reads that integration's action configuration and documentation to identify the required fields. It then suggests and adds the relevant form fields automatically — so even a simple prompt with just the application name and intent can produce a complete, well-structured form.

Workflow Nodes

The AI assembles the appropriate nodes on the Flow Builder canvas, including Trigger, Input, Approval, Action, Decision, Branch/Merge, Delay, End, and the following:

Mapper — Maps and transforms data between nodes or external systems. Commonly used to restructure data from one format to another before passing it to an integration.
Function — Runs custom JavaScript logic for data transformation, computation, or conditional evaluation that goes beyond what Decision nodes offer.
Update — Modifies form data or workflow variables mid-execution without requiring a new Input node.

Edges & Logic

Connections between nodes with appropriate conditions and routing rules are generated automatically based on the logic described in your prompt.

AI-Assisted Action Node Configuration

When the AI generates an Action node that connects to an external integration (e.g., HRIS, ITSM, CRM), it doesn't just place a placeholder — it walks through the full configuration setup:

Application & Connection Selection The AI identifies the relevant integration application based on your prompt (e.g., Workday, ServiceNow, SAP SuccessFactors). It then asks you to select or confirm an active connection — the authenticated credential linking Leena AI to that external system.
Action Discovery Once the application and connection are set, the AI fetches the available actions for that integration (e.g., "Create Employee," "Submit Time Off Request," "Fetch Leave Balance") and selects the most relevant one based on your workflow description.
Dynamic Field Identification The AI reads the selected action's configuration and documentation to determine the exact fields required — field names, data types, required vs. optional, dropdown options, and contextual help text. It uses this information to set up the correct input fields for the Action node.
Auto-Fill from Workflow Context After identifying the required fields, the AI can automatically populate them using data already available in the workflow — such as form submissions from earlier nodes, global constants, or system variables. This auto-fill step maps existing workflow data to integration fields, reducing manual configuration.
Auto-Test Once the Action node is configured and fields are mapped, the AI automatically tests the action to verify that the connection is working and data flows correctly. You can review the test results and adjust field mappings if needed before publishing the workflow.

This means that for supported integrations, the AI handles the end-to-end setup — from picking the right app and action to mapping fields — so you spend less time on repetitive configuration.

Chat History

Every AI workflow generation session is preserved as a chat history that you can revisit and continue.

Accessing Chat History

When you navigate to an AI-generated workflow app, the chat interface loads the full conversation history from that session — including your original prompt, the plan you approved, any follow-up refinements, and the AI's responses. Messages are loaded in reverse chronological order and paginated automatically as you scroll up to view older messages.

Resuming a Session

If you leave the chat mid-conversation, you can return to the same session later. The system automatically reconnects to the existing session associated with the app. You can pick up where you left off — send new messages to continue refining the workflow, and the AI retains the full context of the previous conversation. If you return mid-build, the side-by-side canvas resumes refreshing as the AI continues working.

Session Scope

Each AI chat session is tied to a specific workflow app. Starting a new workflow from the homepage creates a new session. You cannot merge sessions across different apps.

Switching to Manual Editing

You can switch from AI-assisted editing to the manual Flow Builder at any point by clicking "Edit manually" in the app header.

Important: Once you choose to edit a workflow manually, it is permanently detached from AI. You will not be able to regenerate or modify the workflow using the AI chat after this point. A confirmation dialog warns you before this action is taken.

Similarly, once a workflow is published, subsequent edits to the new draft follow the same rule — if you edit the draft manually, AI editing is no longer available for that version.

This restriction ensures that manually introduced changes (which the AI may not be aware of) are not accidentally overwritten by AI-driven modifications.

Tips for Better Results

Be specific about data fields. Instead of "collect employee details," say "collect employee name, employee ID, department, and designation."
Mention approvers explicitly. For example, "send to the reporting manager for approval" gives the AI clear routing instructions.
Describe conditions clearly. "If the amount is greater than ₹10,000, route to the finance head; otherwise, auto-approve" is much more effective than "add some approval logic."
Attach reference documents. If you have an existing SOP, policy document, or process flowchart, upload it. The AI uses the content to generate a more accurate workflow.
Iterate on the plan before approving. It's much cheaper to fix the plan in chat than to fix the built workflow. Push back on anything that doesn't match your intent — the planner is designed to re-present the full updated plan after every change.
Provide a workflow name upfront. When the planner asks for a name and identifier, give one if you have a naming convention. This avoids renaming the app afterwards.
Iterate through chat after build, too. The first generation is a starting point. Use follow-up messages to refine — "add a notification step after approval," "make the department field a dropdown," or "add a delay of 2 days before escalation." The canvas will refresh after each change.

Limitations (Beta)

AI-generated workflows currently support form-based (custom) apps only. Utility apps and List View apps must be created manually.
Some advanced node types — including For Loop, While Loop, and Async Callback — are not yet supported by AI generation. To use these nodes, switch to the Flow Builder and add them manually.
Once a workflow is published or manually edited, it can no longer be modified using the AI chat. You must use the Flow Builder and Form Builder for further changes.
The AI generates a draft — review the workflow carefully before publishing, especially approval routing, conditions, and form validations.
File attachments provided as context are processed for content understanding but are not embedded into the workflow itself.

FAQs

Can I edit the AI-generated workflow manually? Yes. You can switch to the Flow Builder at any time. However, once you edit manually, the workflow is detached from AI and can no longer be modified through the chat interface.

Can I skip the plan review step? No. The plan review is a required checkpoint — the workflow is not built until you explicitly approve the plan in chat. This is by design, to prevent the AI from building something that doesn't match your intent. However, you can approve a plan in a single message (e.g., "Looks good") if you're satisfied with the first proposal.

What if I don't give the workflow a name during planning? The AI will generate a set of candidate names from your prompt and pick the first available one. You can rename the app from settings later.

Do I have to keep the chat open to watch the workflow get built? No. The side-by-side canvas refreshes automatically as the AI commits each change — you can watch it live, or step away and return later. If you navigate back to the app, the canvas will reflect the current state of the build.

Does AI generation consume any credits or tokens? During the beta period, AI generation is included at no additional cost. This may change based on the pricing model in the general availability release.

Can I go back to the old app listing homepage? All existing apps remain accessible from the Apps listing page within Workflows Studio. You can continue to create and manage apps manually from there.

What happens if the AI doesn't understand my prompt? The AI will respond through the chat interface with clarifying questions or a best-effort attempt. You can refine your description and continue the conversation to get closer to the desired result.

Can I resume an AI chat session later? Yes. Your chat history is saved and tied to the workflow app. Navigate back to the app to reload the conversation and continue refining.

Is my data used to train the AI model? No. Your workflow descriptions and attached documents are processed in real-time to generate the workflow and are not used for model training.