Rule-based Logic

Specifications

Basic Settings

AI agents offer an innovative way to efficiently handle customer inquiries and automate standard processes.

Thanks to flexible configuration, agents can be precisely tailored to individual requirements. The creation is done in a few, clearly structured steps, described under the tabs listed above.

1. Define Specifications

This is the foundation for the AI agent. This includes:

Name: The agent’s name should reflect its remit, for example, “Instalment-Agent”.
Description: A short description helps grasp the purpose of the agent at a glance.
Topic: The topic arises from the available skill tags. For instance, an instalment agent could be assigned the skill tag “Payment Transactions”. The assignment ensures a clear organisational categorisation and, for example, simplifies access via the template catalogue.
Channels: Determine which communication methods the AI agent should operate – Email, Chat, Letter, Telephone, etc. An agent can easily be assigned to multiple channels.

2. Choose an Avatar

The avatar gives the AI agent a visual identity and emphasizes its individual character. Various options are available:

Style: Should the avatar appear more professional, friendly, or neutral?
Colour accents: Additionally, colours can be chosen that, for example, match the brand identity.
Recognition value: A uniform avatar across all channels enhances the visual appearance of the AI agent.

3. Configure Intelligence

This is where it’s decided how the AI agent solves tasks:

Smart argumentation logic: Rules in natural language without programming – ideal for standard processes.
Rule-based logic: Code-based workflows for more complex procedures.

4. Adjust Personality

The AI agent’s personality defines how it communicates – not just in content, but also in tone. The communication style can be fine-tuned using intuitive sliders:

Informal to formal: Should the AI agent be casual or businesslike?
Speaking style: From brief answers to detailed explanations.
Concise to comprehensive: Determines the level of detail in the feedback.

These adjustments ensure that the AI agent perfectly matches the company’s language and positively supports the customer experience.

All specifications – name, topic, intelligence, personality, and channels – can be adjusted flexibly at any time. This keeps the AI agent dynamic and always adjusted to current requirements.

Specifications

Basic Settings

AI agents offer an innovative way to efficiently handle customer inquiries and automate standard processes.

1. Define Specifications

This is the foundation for the AI agent. This includes:

Name: The agent’s name should reflect its remit, for example, “Instalment-Agent”.
Description: A short description helps grasp the purpose of the agent at a glance.
Topic: The topic arises from the available skill tags. For instance, an instalment agent could be assigned the skill tag “Payment Transactions”. The assignment ensures a clear organisational categorisation and, for example, simplifies access via the template catalogue.
Channels: Determine which communication methods the AI agent should operate – Email, Chat, Letter, Telephone, etc. An agent can easily be assigned to multiple channels.

2. Choose an Avatar

The avatar gives the AI agent a visual identity and emphasizes its individual character. Various options are available:

Style: Should the avatar appear more professional, friendly, or neutral?
Colour accents: Additionally, colours can be chosen that, for example, match the brand identity.
Recognition value: A uniform avatar across all channels enhances the visual appearance of the AI agent.

3. Configure Intelligence

This is where it’s decided how the AI agent solves tasks:

Smart argumentation logic: Rules in natural language without programming – ideal for standard processes.
Rule-based logic: Code-based workflows for more complex procedures.

4. Adjust Personality

The AI agent’s personality defines how it communicates – not just in content, but also in tone. The communication style can be fine-tuned using intuitive sliders:

Informal to formal: Should the AI agent be casual or businesslike?
Speaking style: From brief answers to detailed explanations.
Concise to comprehensive: Determines the level of detail in the feedback.

These adjustments ensure that the AI agent perfectly matches the company’s language and positively supports the customer experience.

All specifications – name, topic, intelligence, personality, and channels – can be adjusted flexibly at any time. This keeps the AI agent dynamic and always adjusted to current requirements.

Detection

Methods and Configuration

In the Detection section, it is defined when an AI agent is assigned to a ticket. This can be based on keywords, ticket data or AI analysis.

Authentication

If this option is activated, requests from non-authenticated customers will be ignored by the AI agent. This setting is particularly suitable for processes involving sensitive data or requiring customer verification.

Detection Methods

Various methods are available for assigning AI agents:

AI Detection

Condition Detection

AI and Condition Detection

AI or Condition Detection

Manual Detection Only

Input Parameters

Purpose and Configuration

Rule-based AI agents use defined input parameters to extract information from customer inquiries or other data sources and process them further. These parameters control which data is collected, analyzed, and used for processing.

Parameter Management

1. Input Parameters and Their Sources

Input parameters can be taken from various sources:

Ticket data: Information from the original ticket
Customer data: Master data of the customer
Contract data: Information about the customer’s contract
AI Extraction: Automatic AI analysis of the customer’s request to identify relevant information.
Manual setting: A fixed value defined by a user.

2. Parameter Values and Mandatory Fields

Each parameter requires a value that is either automatically extracted or manually entered depending on the source. Mandatory fields ensure that certain information is always present.

3. Parameter Attributes

The following fields are available when configuring a parameter:

Field	Description
Reference to Source Key	Technical key for identification in the data object (e.g. `channel`)
Variable Name in Business Logic	Internal identifier for further processing in logic and actions
Designation for User	Display name in the UI (if visible)
Format	Data type: e.g. `String`, `Boolean`, `Number`, `Date`
Internal Description	Explanation of function or use (only visible internally)
Required	Does this parameter need to be set? Selection: `Yes` / `No`
Visibility for User	Control of UI display: `Visible`, `Hidden`, `Read-Only`

4. Visibility and Editing Restrictions

Visible: The parameter is visible to editors.
Hidden: The parameter is processed in the background but not displayed.
Read-Only: The value is visible, but cannot be changed.

5. Necessity of a Parameter

Yes: The parameter must necessarily be specified so that processing can take place with or through the AI agent.
No: The parameter is optional and can be left blank.

Parameters can be removed at any time.

Example Bank Details

To process customer concerns about bank details adjustments, the bank data agent needs a combination of certain information and checks. It obtains the relevant information via the input parameters, while the checks are defined in the business logic.

Hence, the following input parameters are configured for this AI agent:

contractId (Source: Contract data) → The customer’s contract number to assign the change to the right contract.
newIBAN (Source: Extraction from the customer request with AI) → The new bank account details the customer wishes to deposit.
oldIBAN (Source: Contract data) → The previously deposited IBAN for comparison with the new one.
accountHolder (Source: Extraction from the customer request with AI) → The name of the account holder to verify the identity. If no name is mentioned, the value remains empty.
date (Source: Extraction from the customer request with AI) → If specified, the date from when the new bank details should be valid.

Summary

The predefined configuration of input parameters allows rule-based AI agents to process customer requests efficiently. In combination with the business logic, the data is structured, checked and automatically processed, resulting in precise processing with minimal manual effort.

Business Logic

Functionality and Example

The business logic defines how an AI agent processes input parameters and makes decisions based on them. It ensures that customer inquiries are structured, checked, processed and correctly executed.

Working principle

After the relevant input parameters have been captured, the business logic takes over the processing. This includes:

Checking for completeness and plausibility
Data validation based on predefined rules
Determination of additional values, if necessary
Triggering of actions based on the results of the checks

Technical Implementation

The business logic can be implemented in:

PHP (8.2)
Python (3.11)
JavaScript (Node 20)

The implementation follows a structured processing pipeline:

1. Initialization and Input Validation

Input parameters are taken from the context and converted into a standard format.
Mandatory fields are checked, missing values are replaced with defaults, if necessary.
Type conversions (e.g. bool, int, float) are done to ensure consistent processing.

2. Rule-based Processing

The business logic validates the inputs based on defined rules (e.g. Format checks, plausibility checks).
If necessary, external API calls or database queries are made to supplement additional information.
Calculations and decision processes are carried out based on the parameters (e.g. Branching in case of varying inputs).

3. Actions and Output of Results

The business logic controls further processing by:
- Making automatic changes in the system
- Calling external APIs (e.g. for data storage)
- Generating queries or confirmations
The return is standardized as an answer object, which depending on the context, can contain a confirmation, error messages, or interaction options.

The business logic supplies results that are further processed via output handling. It determines how the AI agent reacts to certain scenarios - whether through confirmations, queries or interaction possibilities.

Example: Business Logic for Processing an IBAN Change

An AI agent processes requests for changing bank details. The business logic ensures that the change is correctly executed and all relevant checks are carried out.

1. Validate Inputs

First, the inputs are standardized and checked:

Remove spaces in the IBAN
If no account holder is specified, it is supplemented from the contract data

$this->input->newIBAN = str_replace(' ', '', $this->input->newIBAN);
$this->input->oldIBAN = $this->input->oldIBAN ?? '';
$this->input->oldIBAN = str_replace(' ', '', $this->input->oldIBAN);

// If no account holder is specified, take from contract data
if (!($this->input->accountHolder ?? null)) {
$this->input->accountHolder = sprintf(
    '%s %s',
    $this->contractData->firstname,
    $this->contractData->lastname
);
}

2. Validate Inputs

The business logic checks whether the new IBAN is correct:

IBAN Validation: Format and check digit verification
Contract Exists: The change must be assigned to a valid contract

// Retrieve contract
$this->contractData = ApiEnneo::getContract($this->input->contractId);
if (!$this->contractData) {
    throw new Exception('Contract not found.');
}

// Format and check digit validation of the IBAN
if (!$this->validateIbanFormatting($this->input->newIBAN) || !$this->validateIbanChecksum($this->input->newIBAN)) {
    $this->interaction->infos[] = new IntentInfo(
        type: 'warning',
        message: 'IBAN not in correct format or checksum invalid',
    );
    $this->interaction->options[] = new IntentOption(
        type: self::ACTION_IBAN_INVALID,
        name: 'Ask customer for correct IBAN',
        recommended: true,
    );
    throw new ChangeBankDataException();
}

3. Output Results and Determine Next Actions

Depending on the results of the checks, the business logic decides how the process continues:* If the checks are successful → IBAN is saved

If errors are detected → Customer receives a query or opportunity for correction

if ($this->input->_action === self::ACTION_ENTER_INTO_SYSTEM) {
    $this->saveBankData();
    foreach ($this->form->fields as $field) {
        $field->readonly = true;
    }
} else {
    $this->interaction->options[] = new IntentOption(
        type: self::ACTION_ENTER_INTO_SYSTEM,
        name: 'Store banking information in the system',
        recommended: true,
    );
}

4. Interaction design with the SDK

Interactions are the primary tool of Enneo to provide structured feedback to agents. An interaction consists of four elements:

Infos: What messages or warnings should be displayed to the agent?
Form: What input fields, e.g. text fields or dropdown menus, should be displayed?
Data: What values do the input fields have?
Options: What buttons should be displayed to the user?

To create an interaction, the Enneo SDK can be used, a library with object definitions. The above interaction can be created with this code:

Note: The Enneo SDK requires environment variables specifying the Enneo API URL and a session token for authorization. If a source code executor is used, these environment variables are inserted at runtime and do not need to be set manually. If the SDK is integrated into its web service, ENNEO_API_URL must be set to https://instance-name.enneo.ai and ENNEO_SESSION_TOKEN to a service worker token.

<?php

use EnneoSDK\Interaction;
require(getenv()['SDK']);

$interaction = new Interaction(data: $in);
$interaction->infos[] = new IntentInfo(
    type: 'warning',
    message: 'The contract was cancelled before the start of supply and therefore cannot be terminated.'
);
$interaction->options[] = new IntentOption(
    type: 'termination_already_processed',
    name: 'Inform customers about this',
    recommended: true
);

Instead of the SDK, the JSON object for the interaction can also be created directly. Here is a complete example of the interaction of the termination AI functionality shown above:

{
    "data": {
          "date": "2023-12-08",
          "type": "regular",
          "dryRun": "true",
          "_action": "null",
          "contractId": 756852,
          "dateReceived": "2023-12-08",
          "proofIncluded": false
      },
    "form": {
          "fields": [
              {
                  "id": "contractId",
                  "type": "integer",
                  "label": "Contract number",
                  "fields": null,
                  "hidden": true,
                  "options": null,
                  "readonly": false,
                  "valueRef": "data.contractId",
                  "validation": null,
                  "placeholder": null,
                  "defaultValue": null
              },{
                  "id": "date",
                  "type": "date",
                  "label": "Termination date",
                  "fields": null,
                  "hidden": false,
                  "options": null,
                  "readonly": false,
                  "valueRef": "data.date",
                  "validation": null,
                  "placeholder": null,
                  "defaultValue": null
              },{
                  "id": "type",
                  "type": "select",
                  "label": "Type of termination",
                  "fields": null,
                  "hidden": false,
                  "options": [
                      {
                          "id": "regular",
                          "label": "Regular",
                          "value": "regular"
                      },
                      {
                          "id": "priceAdjustment",
                          "label": "Price adjustment",
                          "value": "priceAdjustment"
                      },
                      {
                          "id": "relocation",
                          "label": "Relocation",
                          "value": "relocation"
                      },
                      {
                          "id": "death",
                          "label": "Death",
                          "value": "death"
                      },
                      {
                          "id": "custom",
                          "label": "Others",
                          "value": "custom"
                      }
                  ],
          "readonly": false,
          "valueRef": "data.type",
          "validation": null,
          "placeholder": null,
          "defaultValue": null
      },
      {
          "id": "_action",
          "type": "text",
          "label": "_action",
          "fields": null,
          "hidden": true,
          "options": null,
          "readonly": false,
          "valueRef": "data._action",
          "validation": null,
          "placeholder": null,
          "defaultValue": null
      },
      {
          "id": "proofIncluded",
          "type": "checkbox",
          "label": "Proof included",
          "fields": null,
          "hidden": false,
          "options": null,
          "readonly": false,
          "valueRef": "data.proofIncluded",
          "validation": null,
          "placeholder": null,
          "defaultValue": null
      },
      {
          "id": "dryRun",
          "type": "checkbox",
          "label": "(actually execute write access)",
          "fields": null,
          "hidden": true,
          "options": null,
          "readonly": false,
          "valueRef": "data.dryRun",
          "validation": null,
          "placeholder": null,
          "defaultValue": null
      },
      {
          "id": "dateReceived",
          "type": "date",
          "label": "Termination receipt",
          "fields": null,
          "hidden": true,
          "options": null,
          "readonly": false,
          "valueRef": "data.dateReceived",
          "validation": null,
          "placeholder": null,
          "defaultValue": null
      },
      {
          "id": "sourceCode-0",
          "type": "dict",
          "label": "Source code executor response",
          "fields": null,
          "hidden": false,
          "options": null,
          "readonly": false,
          "valueRef": "data.sourceCode-0",
          "validation": null,
          "placeholder": null,
          "defaultValue": null
      }
          ]
      },
          "infos": [
              {
                  "code": null,
                  "type": "warning",
                  "message": "The contract was cancelled before the start of supply and therefore cannot be terminated.",
                  "extraInfo": null
              }
          ],
          "options": [
              {
                  "icon": "check",
                  "name": "Inform customers about this",
                  "type": "termination_already_processed",
                  "order": 1,
                  "handler": "",
                  "recommended": true
              }
   ]
}

Summary

The business logic defines how an AI agent processes input parameters and makes decisions based on them. It ensures that customer requests are checked, validated and processed in a structured manner. Automated rules and processes efficiently manage and execute processes transparently, minimizing manual interventions.

Output handling

Functionality and Example

The Output handling defines how the AI agent reacts to the results of the business logic. It determines whether and how information is returned to the user or a system.

Basic functionality

Output handling is based on predefined rules that build on the results of the business logic. It controls among other things:

Text templates: Automatic messages to the user, e.g. confirmations or queries.
Interactions: Provision of buttons or forms for further processing.
API calls: Forwarding the results to other systems.
Automatic ticket actions: Entries into the system or closing of processes.

In Enneo, there are various action types that can trigger a reaction:

AI reply suggestion: The AI generates a reply to the customer based on the context.
Use text template: A defined message is sent directly.
Interaction: The user is given options for further processing.
Close ticket without answering: The request is automatically completed.
Send text template and close ticket: A confirmation is sent, and the ticket is closed.

Example: Output handling in the banking data agent

The banking data agent’s business logic makes decisions based on the input parameters. The Output handling builds on this and controls the reaction.

1. IBAN is already in the system

If the new IBAN is already recorded (iban_already_in_system), a text template is automatically sent to the customer:

Thank you very much for submitting the bank data.

The bank account with the final digits ... {{last4digits newIBAN}} is already stored in our system and will be used by us
{{#if payoutOnly}}for future credits{{else}}for monthly installments, invoices and possible credits{{/if}}.

Action: Use text template
Condition: _action = iban_already_in_system
Automatic execution: Yes → Message is sent fully automated.

2. IBAN is invalid

If the business logic recognizes the new IBAN as invalid (iban_invalid), an alternative action is executed. The customer receives a query to provide a correct IBAN.

The specified IBAN {{newIBAN}} is not valid.
```    Please check the IBAN for possible typographical errors and send us the correct bank details so we can record them. Thank you for your support.

Action: Use interaction or text template
Condition: _action = iban_invalid
Automatic Execution: No → The user decides on the course of action.

3. IBAN has been successfully recorded

As soon as the IBAN has been successfully entered into the system (enter_into_system), a confirmation will be sent to the customer:

thank you for your message.

The bank details with the last digits ...{{last4digits newIBAN}} have been recorded in our system and will be used by us 
{{#if payoutOnly}}for future credits{{else}}for monthly deductions, invoices and possible credits{{/if}}.

Action: Use text template
Condition: _action = enter_into_system
Automatic Execution: Yes → Message is sent directly.

Summary

The output management connects business logic with communication. It ensures that decisions are automatically transformed into actions – be it through direct confirmations, queries or follow-up processes. Thus, customer inquiries are handled efficiently, comprehensibly and without manual intervention.

Testing and Publishing

Operating Mechanism

Test cases simulate real scenarios to ensure that the AI agent is functioning correctly. Each test case is based on a ticket ID and reflects the entire processing process. Regular tests ensure that the AI agent operates stably and reliably, even with changed requirements or system updates.

Test Procedure

A real ticket ID is selected and added.
The AI agent processes the ticket based on the defined logic.
The result is displayed as successful or failed.

Result Interpretation

Successful: The test confirms that the AI agent is responding as intended. The AI agent can now be published.
Failed: In this case, the existing settings need to be examined. Instructions, detections, input parameters, business logic, and/or output handling should be investigated for possible inconsistencies. After making the adjustment, the test should be run again.

enneo Documentation

Guides

AI-Agents

FAQ

Specifications

1. Define Specifications

2. Choose an Avatar

3. Configure Intelligence

4. Adjust Personality

Specifications

1. Define Specifications

2. Choose an Avatar

3. Configure Intelligence

4. Adjust Personality

Detection

Authentication

Detection Methods

Input Parameters

Parameter Management

Example Bank Details

Summary

Business Logic

Working principle

Technical Implementation

Example: Business Logic for Processing an IBAN Change

Summary

Output handling

Basic functionality

Example: Output handling in the banking data agent

Summary

Testing and Publishing

Test Procedure

Result Interpretation

enneo Documentation

Guides

AI-Agents

FAQ

​Specifications

​1. Define Specifications

​2. Choose an Avatar

​3. Configure Intelligence

​4. Adjust Personality

​Specifications

​1. Define Specifications

​2. Choose an Avatar

​3. Configure Intelligence

​4. Adjust Personality

​Detection

​Authentication

​Detection Methods

​Input Parameters

​Parameter Management

​Example Bank Details

​Summary

​Business Logic

​Working principle

​Technical Implementation

​Example: Business Logic for Processing an IBAN Change

​Summary

​Output handling

​Basic functionality

​Example: Output handling in the banking data agent

​Summary

​Testing and Publishing

​Test Procedure

​Result Interpretation

Specifications

1. Define Specifications

2. Choose an Avatar

3. Configure Intelligence

4. Adjust Personality

Specifications

1. Define Specifications

2. Choose an Avatar

3. Configure Intelligence

4. Adjust Personality

Detection

Authentication

Detection Methods

Input Parameters

Parameter Management

Example Bank Details

Summary

Business Logic

Working principle

Technical Implementation

Example: Business Logic for Processing an IBAN Change

Summary

Output handling

Basic functionality

Example: Output handling in the banking data agent

Summary

Testing and Publishing

Test Procedure

Result Interpretation