Headless Operation

Built for headless operation from day one

Every configuration option in Enta is exposed as a tested tool. Every operation a human performs through the visual editors, an AI agent performs through the same contract. No screen-scraping. No private back doors. The same path, validated end-to-end.

  • Every operation is a tool — schema, expressions, UI, workflows, security, imports
  • Every tool has tests — behavior is guaranteed before AI gets to use it
  • Same validation, same security, same audit trail — human or agent
  • Round-trips cleanly — what AI produces, a human can open and edit in the visual designer

AI with complete, structured knowledge

Enta's AI doesn't guess. It knows your entire application definition before generating a single line.

  • Knows every table, column, relationship, and constraint

    Database schema can be part of the context when you provide it -- not a prerequisite.

  • Knows every Business Object, property, and validation rule

    Domain semantics, computed fields, and constraints are all explicit.

  • Knows every UI layout, data binding, and event subscription

    Your forms, grids, and dashboards are structured definitions, not opaque markup.

  • Knows every workflow step, trigger, and approval rule

    Process logic, branching conditions, and escalation paths are all machine-readable.

  • Knows the security matrix -- who can see what, who can do what

    Roles, permissions, data filters, and row-level access are fully declared.

This isn't AI generating code in a vacuum. It's AI with complete context.

Schema & Domain Understanding

From raw tables to typed domain models

Enta's AI analyzes your existing database and suggests Business Objects, inheritance hierarchies, and relationships. It detects patterns that take humans hours to catalog: header/detail pairs, lookup tables, junction tables, soft-delete flags.

  • Automatic type inference and label generation
  • Relationship and FK detection from naming conventions
  • Pattern recognition across hundreds of tables
Before
-- Raw database column
CUST_HDR.CRTD_DT  DATETIME  NULL
After
// Generated Business Object property
Customer.CreatedDate
  Type:     DateTime
  Label:    "Created Date"
  Format:   "dd MMM yyyy"
  ReadOnly: true
Natural Language Prompt

"Show me all overdue invoices grouped by customer with total outstanding"

Generated SQL
SELECT
    c.Name                AS Customer,
    COUNT(i.Id)           AS InvoiceCount,
    SUM(i.AmountDue)      AS TotalOutstanding
FROM Invoice i
JOIN Customer c ON c.Id = i.CustomerId
WHERE i.DueDate < GETDATE()
  AND i.Status <> 'Paid'
GROUP BY c.Name
ORDER BY TotalOutstanding DESC
Expression Generation

Natural language to visual SQL

Describe what you need in plain English. The AI generates a fully structured expression -- not raw text, but a visual query in the expression builder that you can refine, validate, and execute.

  • Query generation with JOINs, CTEs, and window functions
  • Query optimization suggestions
  • Validation rule generation from business requirements
UI Generation

Schema-aware layout generation

The AI reads your Business Object definitions and picks the right control for each property. Foreign keys become dropdowns. DateTime fields become date pickers. Boolean fields become checkboxes. No manual wiring required.

  • Automatic control selection from property types
  • Dashboard generation from a text description
  • Responsive adaptation based on field importance
Schema Input
Customer
  Name           String
  Email          String    [Email]
  Country        FK       -> Country.Id
  DateOfBirth    DateTime
  IsActive       Boolean
Generated Layout
Text input
Email input with validation
Dropdown (FK lookup)
Date picker
Checkbox / Toggle
Description

"When an order is placed, notify the warehouse, wait for confirmation, generate invoice"

Generated Workflow
Trigger: Order Created
Notify Warehouse Team
Wait for Confirmation
Generate Invoice
+ Error handling, retry logic, escalation (auto-generated)
Workflow Generation

Process from description

Describe a business process and the AI generates a complete workflow definition -- triggers, steps, approval chains, error handling, and escalation paths. Built on Enta's 30+ step type library, not generic code generation.

  • Approval chain scaffolding from org structure
  • Automatic error handling and retry logic
  • Escalation rules based on timeout and conditions
Testing

Test generation from your full app definition

Because Enta knows your entire application -- schema, rules, UI, workflows, and security -- the AI can generate end-to-end tests that cover real business scenarios, not just happy paths.

  • End-to-end test generation from full app definition
  • Edge case detection from declarative rules and constraints
  • Security testing across all roles and access paths
App Definition Input
// Known to the AI:
BusinessObject: Invoice
Rule: Amount > 0
Rule: DueDate > CreatedDate
Security: SalesRep sees own invoices only
Workflow: Approval required if Amount > 10,000
Generated Tests
test_create_invoice_valid
test_create_invoice_zero_amount     // edge
test_create_invoice_past_due_date  // edge
test_salesrep_sees_own_only       // security
test_admin_sees_all              // security
test_approval_required_over_10k    // workflow
test_no_approval_under_10k         // workflow
User Request
“When an order over $5,000 comes in, notify the regional manager, wait for their approval, then send the confirmation to the customer.”
Generated Workflow
Trigger: Order created where Amount > 5,000
Step: Resolve regional manager from customer region
Step: Request approval from manager
Step: On approval, send confirmation email
Status: Pending review Submit for approval →
User-Defined Workflows

Your users automate their own processes

With permission, end users describe a business process in plain language. Enta produces a workflow definition they can review and refine, then submits it through your standard approval and deployment pipeline. Once deployed, it runs as part of the application.

The same engine that runs admin-authored workflows runs user-authored ones. No second-tier runtime. No sandbox. Same triggers, same step types, same security.

  • End users describe processes in plain language
  • Generated workflows go through the standard approval and deployment pipeline
  • Permissions control who can author and who can approve
  • True process automation — written by the people who run the process

See AI in action

Start with an agent-readable demo: tested public contract, validated output, and a live app preview.

See Demo Options