Collect Data

The Collect Data tool lets your AI agent gather specific information from callers-email, phone, license plate, address, or any custom field-with built-in validation and confirmation. A specialized sub-agent temporarily takes over: it asks for the data, handles voice quirks (“dot” for ”.”, letter-by-letter spelling), validates, reads it back, and only accepts it once the caller explicitly agrees. Then it hands control back to your main agent with the confirmed value.

Data Types

telli has five built-in data types, each with its own specialized sub-agent:

Type	What it collects	Validation
Email	An email address; converts “at” to `@`, “dot” to `.`, spells back letter by letter	Valid email format ([email protected])
Digits	A number sequence-IDs, codes, PINs, phone numbers	Digits only; optional min/max length
License Plate	A vehicle plate (currently German plates only)	Parsed against official format; offers city-name disambiguation
Address	Full postal address (street, city, country, optional postal code)	Checked against Google Address Validation API
Custom	Free-form field for anything else (contract numbers, serials, company names)	Optional custom constraints (see below)

How It Works During a Call

Main agent calls the tool-Based on your prompt, it calls collect_data with the relevant key(s).
Sub-agent takes over-A type-specific sub-agent takes control; the main agent pauses.
Collection flow-The sub-agent asks for the data, processes voice quirks, validates, re-asks on failure, reads the value back, and waits for explicit confirmation in a separate speech turn (so it can’t confirm its own readback).
Outcome-Either confirmed (caller agreed) or declined (caller refused, with a reason).
Main agent resumes-Control returns with the value, and the main agent is instructed to never re-ask or re-confirm an already-confirmed value.

You can collect multiple fields in one call (e.g. @collect_data for email and case_number together); the sub-agents run in sequence within the same task group.

Behavior Modes
Validation Constraints

Every task has a behavior setting:

Auto (default)-The sub-agent runs its built-in collection flow (ask, validate, read back, confirm). Optimized for accurate voice collection; no configuration needed. Best for most use cases.
Custom Prompt-Additional instructions (max 4,000 characters) appended to the built-in flow. Lets you customize how the agent collects without replacing the core validation. For example, greet the caller by name first, reassure a hesitant caller, or ask them to read a code slowly one digit at a time.

Custom prompts are appended, not a replacement-the core validation and confirmation flow always runs regardless.

With the Custom data type you can add rules; all must pass for the value to be accepted:

Constraint	What it does	Example
Exact Length	Exactly N characters	A 10-character serial number
Min / Max Length	Between a minimum and maximum (set either or both)	Reference number between 4-10 characters
Alphabet	Restrict allowed character types: lowercase, uppercase, numbers, special characters, blanks	Numbers only for a PIN code
Regex	A `fullmatch` pattern plus a human-readable description	Pattern `[A-Z]{2}-\d{6}` with description “two uppercase letters, a dash, then six digits (e.g. AB-123456)”

You can combine multiple constraints, but Exact Length and Min/Max Length are mutually exclusive.

Integration & Prompt

What You Get Back
When to Use It
Prompt Patterns

Collected data appears in the call_ended webhook under collected_data (or null if no tasks were triggered):

{
  "collected_data": {
    "email": {
      "task_type": "email",
      "status": "confirmed",
      "value": "[email protected]"
    },
    "case_number": {
      "task_type": "digits",
      "status": "declined",
      "value": null
    }
  }
}

Reference tasks with @collect_data:key. Common patterns:

## Data Collection

# Basic
When the caller needs to update their email, use @collect_data:email.

# Conditional
Only collect email (@collect_data:email) if they ask for a confirmation to be sent.
Always collect the case number (@collect_data:case_number) at the start of every call.

# With fallback
Try @collect_data:email. If they decline, continue and note no email was provided.

# Multiple fields
At the start, collect both @collect_data:customer_id and @collect_data:email for verification.

Outcomes vs. Collected Data

	Collected Data	Call Outcomes
When it runs	During the call	After the call
How it works	Sub-agent asks the caller directly	AI reads the transcript
Caller involved?	Yes-they provide and confirm	No-fully automated
Accuracy	Very high (validated + confirmed)	Depends on transcript and instructions
Best for	Emails, IDs, codes, addresses	Sentiment, intent, summaries, classifications
Format	Always a string	Boolean, string, number, category, multi-category

Use both together: collect the email with @collect_data:email (verified), and use a call outcome to score whether they’re a qualified lead (AI analysis).

Workflows-Automating What Happens After the Call

Workflows are telli’s visual automation system: define what happens after a call ends-send a webhook, update your CRM, fire an email, schedule a follow-up-based on collected data and call outcomes. Each workflow is linked to a specific agent and triggers on Call Ended (call data available) or Contact Created (no call data).

Data Available in Workflows
Example

A Call Ended workflow can reference everything from the call; values are unwrapped automatically ({{callOutcome.sentiment}} gives "positive" directly):

Category	Reference Syntax	Examples
Collected Data	`{{collectedData.key}}`	`{{collectedData.email}}`
Call Outcomes	`{{callOutcome.name}}`	`{{callOutcome.sentiment}}`
Call Metadata	`{{call.field}}`	`{{call.id}}`, `{{call.duration}}`
Contact	`{{contact.field}}`	`{{contact.firstName}}`, `{{contact.email}}`

[Call Ended]
    ↓
[If: collectedData.email is not null]
    ├── True:
    │   └── [Webhook: POST to your API]
    │       Body: {
    │         "email": "{{collectedData.email}}",
    │         "sentiment": "{{callOutcome.sentiment}}",
    │         "summary": "{{callOutcome.summary}}",
    │         "call_id": "{{call.id}}"
    │       }
    └── False:
        └── [Send SMS: "We missed your email-reply to provide it."]

Full Example

During the Call                          After the Call
─────────────                            ──────────────

Caller provides email                    AI reads transcript
  → @collect_data:email                    → sentiment = "positive"
  → Sub-agent validates + confirms         → appointment_booked = true
  → Stored as collectedData.email          → Stored as callOutcome.*

                    ↓                                    ↓
                    └──────────── Both feed into ────────┘
                                      ↓
                              [Workflow Trigger] "Call Ended"
                                      ↓
                        [Actions] Webhook, CRM, SMS,
                              schedule follow-up...

Collected Data gives verified, caller-confirmed input. Call Outcomes give AI-derived analysis of what happened. Workflows let you act on both-automatically, every time.

Tips and Best Practices

Use descriptive keys-customer_email beats email_2 when reading webhook payloads.
Write clear descriptions-They tell the AI when to use each task. “Collect the email when they request an order confirmation” beats “Email collection.”
Set appropriate constraints-If a code is always 8 characters, set an exact length so partial input is rejected.
Don’t over-collect-Each task adds a formal ask-validate-confirm flow. Reserve it for data where accuracy and structure matter.
Test with voice-The flow is optimized for voice; call your agent and dictate data to check it feels natural.
Use Auto first-Built-in flows are well-tested. Switch to Custom when you need specific adjustments.
Check the status field-Don’t assume every value is confirmed. Handle declined, error, and in_progress gracefully.

Prompting Cookbook

Feature Cookbooks

Data Types

Integration & Prompt

Outcomes vs. Collected Data

Workflows-Automating What Happens After the Call

Full Example

Tips and Best Practices

​Data Types

​Integration & Prompt

​Outcomes vs. Collected Data

​Workflows-Automating What Happens After the Call

​Full Example

​Tips and Best Practices

Data Types

Integration & Prompt

Outcomes vs. Collected Data

Workflows-Automating What Happens After the Call

Full Example

Tips and Best Practices