Use agentic self-service

Agentic self-service enables Connect AI agents to autonomously resolve customer issues across voice and chat channels. Unlike legacy self-service, where the AI agent returns control to the contact flow when a custom tool is selected, agentic self-service uses orchestrator AI agents that can reason across multiple steps, invoke MCP tools to take actions on behalf of customers, and maintain a continuous conversation until the issue is resolved or escalation is needed.

For example, when a customer calls about a hotel reservation, an orchestrator AI agent can greet them by name, ask clarifying questions, look up their booking, and process a modification—all within a single conversation, without returning control to the contact flow between each step.

Key capabilities

Agentic self-service provides the following capabilities:

Tools for orchestrator AI agents

You can configure your orchestrator AI agent for self-service with the following tool types:

Set up agentic self-service

Follow these high-level steps to set up agentic self-service:

Create custom Return to Control tools

Return to Control tools signal the AI agent to stop processing and return control to the contact flow. When a Return to Control tool is invoked, the tool name and its input parameters are stored as Amazon Lex session attributes, which your contact flow can read using a Check contact attributes block to determine the next action.

While the SelfServiceOrchestrator AI agent includes default Complete and Escalate Return to Control tools, you can create custom Return to Control tools with input schemas that capture additional context for your contact flow to act on.

To create a custom Return to Control tool:

Example: Custom Escalate tool with context

The following example shows how to replace the default Escalate tool with a custom version that captures escalation reason, summary, customer intent, and sentiment. This additional context gives human agents a head start when they pick up the conversation.

First, remove the default Escalate tool from your AI agent. Then create a new Return to Control tool named Escalate with the following input schema:

{
    "type": "object",
    "properties": {
        "customerIntent": {
            "type": "string",
            "description": "A brief phrase describing what the customer wants to accomplish"
        },
        "sentiment": {
            "type": "string",
            "description": "Customer's emotional state during the conversation",
            "enum": ["positive", "neutral", "frustrated"]
        },
        "escalationSummary": {
            "type": "string",
            "description": "Summary for the human agent including what the customer asked for, what was attempted, and why escalation is needed",
            "maxLength": 500
        },
        "escalationReason": {
            "type": "string",
            "description": "Category for the escalation reason",
            "enum": [
                "complex_request",
                "technical_issue",
                "customer_frustration",
                "policy_exception",
                "out_of_scope",
                "other"
            ]
        }
    },
    "required": [
        "escalationReason",
        "escalationSummary",
        "customerIntent",
        "sentiment"
    ]
}

In the Instructions field, describe when the AI agent should escalate. For example:

Escalate to a human agent when:
1. The customer's request requires specialized expertise
2. Multiple tools fail or return errors repeatedly
3. The customer expresses frustration or explicitly requests a human
4. The request involves complex coordination across multiple services
5. You cannot provide adequate assistance with available tools

(Optional) Add examples to guide the AI agent's tone during escalation. For example:

<message>
I understand this requires some specialized attention. Let me connect you
with a team member who can help coordinate all the details. I'll share
everything we've discussed so they can pick up right where we left off.
</message>

Handle Return to Control tools in your contact flow

When the AI agent invokes a Return to Control tool, control returns to your contact flow. You need to configure your flow to detect which tool was invoked and route the contact accordingly.

How Return to Control detection works

When the AI agent invokes a Return to Control tool:

Configure routing based on Return to Control tools

Follow these steps to add Return to Control routing to your contact flow:

Example: Routing an Escalate tool with context

If you created a custom Escalate tool with context (see Example: Custom Escalate tool with context), you can copy the escalation context to contact attributes using a Set contact attributes block. Set the following attributes dynamically:

(Optional) Add a Set event flow block to display the escalation context to the human agent when they accept the contact. Set the event to Default flow for agent UI and select a flow that presents the escalation summary, reason, and sentiment to the agent.

Use Constant tools for testing and development

Constant tools return a configured static string value to the AI agent when invoked. Unlike Return to Control tools, Constant tools do not end the AI conversation—the AI agent receives the string and continues the conversation. This makes Constant tools useful for testing and rapid iteration during development, allowing you to simulate tool responses without connecting to backend systems.

To create a Constant tool:

For example, you can create a Constant tool named getOrderStatus that returns a sample JSON response. This lets you test how your AI agent handles order status requests before connecting to your actual order management system through an MCP tool.