Designing Contextual Handoffs from Custom Virtual Agents to Human Representatives

How-To-Articles
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Designing Contextual Handoffs from Custom Virtual Agents to Human Representatives

What This Guide Covers

You are designing an end-to-end handoff protocol for a custom virtual agent (built outside of Genesys Cloud’s native bot framework-for example, using a custom LLM-powered agent built with LangChain, or a proprietary enterprise chatbot) integrating via the Genesys Cloud Open Messaging or Guest Chat APIs. When complete, every bot-to-human escalation will carry a structured “Handoff Manifest”-a compact JSON summary of the conversation context, resolved intents, collected data, and the specific reason for escalation-ensuring the human agent receives full context in under 3 seconds without reading the entire conversation transcript.

Prerequisites, Roles & Licensing

  • Genesys Cloud: Any CX tier with Open Messaging or Guest Chat API access.
  • Permissions required:
    • Conversations > Message > Create, Edit
    • Routing > Queue > View (for direct queue routing)
  • Infrastructure:
    • Your custom bot engine (LangChain, Rasa, a proprietary enterprise bot, etc.).
    • A Genesys Cloud service account with an OAuth Client Credentials grant.

The Implementation Deep-Dive

1. The Handoff Manifest Standard

The core concept is the Handoff Manifest: a structured JSON object that your bot engine computes at the moment of escalation and attaches to the Genesys conversation before transferring it to the queue.

{
  "handoff_manifest": {
    "trigger": "CustomerRequested",
    "trigger_utterance": "I just want to talk to a real person, this is ridiculous.",
    "bot_confidence_at_exit": 0.42,
    "session_duration_seconds": 187,
    "intents_detected": [
      {"intent": "BillingInquiry", "confidence": 0.91, "resolved": false},
      {"intent": "DisputeCharge", "confidence": 0.78, "resolved": false}
    ],
    "slots_collected": {
      "account_number": "ACC-987654",
      "disputed_amount": "$47.99",
      "dispute_date": "2026-05-10"
    },
    "sentiment_trend": "NEGATIVE_ESCALATING",
    "recommended_queue": "BillingDisputes_Tier2",
    "agent_briefing": "Customer wants to dispute a $47.99 charge from May 10. Bot was unable to locate the transaction in the system. Customer became frustrated after 3 failed searches. Recommend empathy-first approach."
  }
}

This manifest is generated by your bot engine and attached to the Genesys conversation as a Participant Data attribute.

2. Attaching the Manifest to the Genesys Conversation

Your bot engine has been managing the conversation via the Genesys Guest Chat API or Open Messaging API. At the moment of escalation, it calls the Genesys API to attach the manifest.

import requests
import json

GENESYS_API = "https://api.mypurecloud.com"

def execute_handoff(
    conversation_id: str,
    participant_id: str,
    handoff_manifest: dict,
    target_queue_id: str,
    access_token: str
):
    """
    1. Attaches the Handoff Manifest to the conversation as Participant Data.
    2. Updates the queue to route to a human agent.
    """
    headers = {
        "Authorization": f"Bearer {access_token}",
        "Content-Type": "application/json"
    }
    
    # Step 1: Serialize and attach the manifest as a participant attribute
    manifest_json = json.dumps(handoff_manifest, separators=(',', ':'))  # Compact JSON
    
    # Genesys Participant Data has a 256-char value limit per key
    # For large manifests, store the full manifest externally and only pass a reference ID
    if len(manifest_json) > 256:
        manifest_ref = store_manifest_externally(handoff_manifest)  # Returns a short UUID
        attributes = {"handoff_manifest_ref": manifest_ref}
    else:
        attributes = {"handoff_manifest": manifest_json}
    
    # Add the agent briefing as a dedicated attribute for quick access
    attributes["agent_briefing"] = handoff_manifest.get("agent_briefing", "")[:256]
    attributes["recommended_queue"] = handoff_manifest.get("recommended_queue", "")
    
    # Patch participant attributes
    requests.patch(
        f"{GENESYS_API}/api/v2/conversations/messages/{conversation_id}/participants/{participant_id}/attributes",
        headers=headers,
        json={"attributes": attributes}
    ).raise_for_status()
    
    # Step 2: Transfer to target queue
    requests.post(
        f"{GENESYS_API}/api/v2/conversations/messages/{conversation_id}/participants/{participant_id}/replace",
        headers=headers,
        json={
            "queueId": target_queue_id,
            "userId": None,  # Route to queue, not specific agent
            "wrap_up_prompt": "optional"
        }
    ).raise_for_status()
    
    print(f"Handoff executed. Conversation {conversation_id} transferred to queue {target_queue_id}.")

3. Rendering the Manifest in the Agent Desktop

The agent desktop must display the Handoff Manifest in a readable, actionable format-not as raw JSON.

Build a custom Genesys Cloud interaction panel widget that:

  1. Reads the handoff_manifest_ref or handoff_manifest attribute from the conversation.
  2. If a ref is present, fetches the full manifest from your external store.
  3. Renders the manifest as a structured “Agent Briefing Card.”

Agent Briefing Card Design:

┌─────────────────────────────────────────────┐
│  🤖 BOT HANDOFF SUMMARY                     │
│─────────────────────────────────────────────│
│  Trigger: Customer Requested                │
│  Sentiment: 🔴 Negative (Escalating)        │
│  Session Duration: 3m 7s                    │
│─────────────────────────────────────────────│
│  COLLECTED DATA:                            │
│  ├ Account: ACC-987654                      │
│  ├ Dispute Amount: $47.99                   │
│  └ Dispute Date: May 10, 2026              │
│─────────────────────────────────────────────│
│  AGENT NOTE:                                │
│  Customer wants to dispute a $47.99 charge. │
│  Bot failed to locate the transaction.      │
│  ⚡ Recommend empathy-first approach.       │
└─────────────────────────────────────────────┘

4. Dynamic Queue Selection Using the Manifest

Instead of routing all escalations to a single generic “Support” queue, use the manifest data to select the optimal queue programmatically.

Your bot engine computes recommended_queue using a decision tree before calling execute_handoff:

def determine_target_queue(manifest: dict, queue_registry: dict) -> str:
    """
    Selects the most appropriate Genesys queue based on the handoff manifest.
    queue_registry: {"BillingDisputes_Tier2": "genesys-queue-id-xxx", ...}
    """
    intents = {i["intent"] for i in manifest.get("intents_detected", [])}
    sentiment = manifest.get("sentiment_trend", "NEUTRAL")
    bot_confidence = manifest.get("bot_confidence_at_exit", 1.0)
    
    # High-priority: Angry customer with unresolved dispute
    if sentiment == "NEGATIVE_ESCALATING" and "DisputeCharge" in intents:
        return queue_registry["BillingDisputes_Priority"]
    
    # Standard billing
    if "BillingInquiry" in intents or "DisputeCharge" in intents:
        return queue_registry["BillingDisputes_Tier2"]
    
    # Bot was completely lost (very low confidence)
    if bot_confidence < 0.30:
        return queue_registry["General_Escalation"]
    
    return queue_registry["General_Support"]

Validation, Edge Cases & Troubleshooting

Edge Case 1: Manifest Exceeds Participant Data Size Limits

A complex bot session with 20 collected slots and a long conversation summary can produce a manifest larger than the 256-character Genesys Participant Data value limit.
Solution: Always store manifests larger than 256 bytes in an external store (DynamoDB) keyed by a UUID. Only store the UUID in the Participant attribute. The agent widget fetches the full manifest using the UUID on load.

Edge Case 2: The Agent Receives the Interaction Before the Manifest Loads

If the agent accepts the interaction in under 1 second (before the widget has fetched and rendered the manifest), they see an empty briefing card.
Solution: Design the widget to show a “Loading bot context…” spinner immediately, and only replace it with the full briefing card once the data is loaded. Additionally, set the agent’s available status to “Not Ready” for 3 seconds after accepting an escalated interaction, giving the widget time to populate. Use Genesys Cloud Interaction Screen Pop rules to configure this behavior.

Edge Case 3: Duplicate Handoff Attempts

If the bot’s network call to execute_handoff times out and the bot retries, it might attempt to transfer the same conversation twice. The second transfer call will fail if the first one already moved the conversation to a human agent.
Solution: Before calling execute_handoff, check the current state of the conversation. If the conversation’s participant.state is already "connected" (agent answered) or "alerting" (agent is being rung), skip the handoff execution. Log the duplicate attempt for investigation.

Official References