Implementing Omnichannel Context Preservation for Long-Lived Asynchronous Interactions

How-To-Articles
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Implementing Omnichannel Context Preservation for Long-Lived Asynchronous Interactions

What This Guide Covers

You are architecting the session context layer for customer interactions that span multiple channels and multiple sessions over an extended period-for example, a customer who opens a web chat, abandons it, calls the next day, and then sends a follow-up email a week later about the same unresolved issue. When complete, your architecture will maintain a Unified Interaction Context record across all channels, ensure that every agent who touches the customer’s interaction-regardless of channel or time-sees the complete history, and enforce configurable context retention windows that align with your privacy policy and GDPR obligations.

Prerequisites, Roles & Licensing

  • Genesys Cloud: CX 2 or 3 with Omnichannel capabilities.
  • Permissions required:
    • Conversations > Conversation > View
    • Analytics > Conversation Detail > View
    • Integrations > Integration > Edit (for webhook/Data Action configuration)
  • Infrastructure:
    • A centralized context store (DynamoDB, Redis, or a purpose-built Customer Journey database).
    • An event-driven pipeline (EventBridge) to capture conversation state changes.
    • A custom agent desktop widget to display the cross-channel context panel.

The Implementation Deep-Dive

1. The Omnichannel Context Problem

The core issue is that Genesys Cloud stores each interaction as a discrete Conversation object with its own conversationId. A customer’s call on Monday and their chat on Tuesday are two separate database records with no native link between them.

Without explicit context preservation, every agent starts blind:

  • Chat Agent (Monday): Helps customer with billing issue, doesn’t resolve it.
  • Voice Agent (Tuesday): Customer calls back. Agent asks: “How can I help you today?” Customer: “Are you kidding me? I already explained this yesterday.”

2. The Unified Interaction Context (UIC) Data Model

Design a centralized Customer Journey record that aggregates context across all interactions.

from dataclasses import dataclass, field
from datetime import datetime
from typing import Literal

@dataclass
class InteractionSummary:
    conversation_id: str
    channel: Literal["voice", "chat", "email", "sms", "whatsapp"]
    start_time: datetime
    duration_seconds: int
    resolution_status: Literal["RESOLVED", "UNRESOLVED", "ESCALATED", "ABANDONED"]
    intent: str
    agent_id: str | None
    outcome_notes: str  # Brief AI-generated summary

@dataclass
class UnifiedInteractionContext:
    customer_id: str                   # Stable identifier (from CRM or IdP)
    customer_phone: str | None         # ANI for lookup
    customer_email: str | None         # Email for lookup
    last_updated: datetime
    active_issue: str | None           # Current unresolved issue description
    active_issue_since: datetime | None
    interactions: list[InteractionSummary] = field(default_factory=list)
    collected_data: dict = field(default_factory=dict)  # Slots from all sessions
    sentiment_history: list[str] = field(default_factory=list)  # ["POSITIVE", "NEGATIVE"]

3. The Event-Driven Context Update Pipeline

When a Genesys conversation concludes, automatically update the UIC record.

Step 1: Subscribe to Conversation End Events
Configure an AWS EventBridge rule (via Genesys Cloud EventBridge integration) to trigger a Lambda on every v2.conversations.{id} disconnect event.

import boto3
import json
from datetime import datetime

DYNAMODB = boto3.resource('dynamodb')
UIC_TABLE = DYNAMODB.Table('unified-interaction-context')

def update_context_on_conversation_end(event: dict, context):
    """
    Triggered by Genesys EventBridge on conversation disconnect.
    Updates the Unified Interaction Context for the customer.
    """
    conv_data = event.get('detail', {})
    conversation_id = conv_data.get('conversationId')
    
    # 1. Fetch full conversation detail from Analytics API
    conv_detail = fetch_conversation_detail(conversation_id)
    
    # 2. Identify the customer
    customer_id = extract_customer_id(conv_detail)
    if not customer_id:
        return  # Anonymous interaction, skip
    
    # 3. Generate AI summary of the interaction outcome
    transcript = extract_transcript(conv_detail)
    summary = generate_interaction_summary(transcript)
    
    # 4. Build the InteractionSummary
    new_interaction = {
        "conversationId": conversation_id,
        "channel": extract_channel(conv_detail),
        "startTime": conv_detail.get("conversationStart"),
        "durationSeconds": calculate_duration(conv_detail),
        "intent": extract_primary_intent(conv_detail),
        "resolutionStatus": determine_resolution(conv_detail, summary),
        "outcomeSummary": summary
    }
    
    # 5. Upsert the UIC record (create if first interaction, update if returning customer)
    UIC_TABLE.update_item(
        Key={"customerId": customer_id},
        UpdateExpression=(
            "SET lastUpdated = :ts, "
            "interactions = list_append(if_not_exists(interactions, :empty_list), :new_interaction)"
        ),
        ExpressionAttributeValues={
            ":ts": datetime.utcnow().isoformat(),
            ":empty_list": [],
            ":new_interaction": [new_interaction]
        }
    )
    
    # 6. Update the active issue if unresolved
    if new_interaction["resolutionStatus"] == "UNRESOLVED":
        UIC_TABLE.update_item(
            Key={"customerId": customer_id},
            UpdateExpression="SET activeIssue = :issue, activeIssueSince = if_not_exists(activeIssueSince, :ts)",
            ExpressionAttributeValues={
                ":issue": new_interaction.get("intent", "Unknown"),
                ":ts": new_interaction["startTime"]
            }
        )
    elif new_interaction["resolutionStatus"] == "RESOLVED":
        UIC_TABLE.update_item(
            Key={"customerId": customer_id},
            UpdateExpression="REMOVE activeIssue, activeIssueSince"
        )

4. Serving Context to the Agent Desktop

When an agent accepts an interaction, the custom widget queries the UIC store and renders a “Customer Journey” panel.

Lookup Strategy (Prioritized):

  1. If the interaction has a customerId attribute (set by a previous CRM data dip in the IVR), use it directly.
  2. Fall back to ANI lookup (phone number).
  3. Fall back to email lookup.
  4. If no match, show an empty panel with a “First Contact” indicator.
def get_customer_context_for_interaction(conversation_id: str, access_token: str) -> dict | None:
    """Retrieves or constructs context for the agent widget."""
    
    # Get current conversation participant data
    conv = get_conversation(conversation_id, access_token)
    customer_participant = next(
        (p for p in conv.get("participants", []) if p.get("purpose") == "customer"), None
    )
    
    if not customer_participant:
        return None
    
    # Try each lookup strategy
    attrs = customer_participant.get("attributes", {})
    customer_id = attrs.get("customerId")
    
    if not customer_id:
        phone = customer_participant.get("ani", "")
        customer_id = lookup_customer_by_phone(phone)
    
    if not customer_id:
        return {"status": "first_contact", "interactions": []}
    
    # Fetch from UIC store
    response = UIC_TABLE.get_item(Key={"customerId": customer_id})
    uic = response.get("Item")
    
    if not uic:
        return {"status": "first_contact", "interactions": []}
    
    return {
        "status": "returning_customer",
        "activeIssue": uic.get("activeIssue"),
        "activeIssueSince": uic.get("activeIssueSince"),
        "recentInteractions": sorted(
            uic.get("interactions", [])[-5:],  # Last 5 interactions
            key=lambda x: x["startTime"],
            reverse=True
        )
    }

Validation, Edge Cases & Troubleshooting

Edge Case 1: Customer ID Doesn’t Exist for Anonymous Interactions

If a customer contacts via web chat without logging in, there is no stable customerId. ANI-based lookup fails because there is no phone number.
Solution: At the start of the web chat, require the customer to enter their email address in the pre-chat form. Use the email as the lookup key. Hash the email before storing it in DynamoDB to avoid storing PII in plaintext.

Edge Case 2: Interaction List Grows Indefinitely

A customer who contacts your center 500 times over 10 years will have a DynamoDB item with 500 interaction objects. DynamoDB has a 400KB item size limit; at ~300 bytes per interaction summary, you hit the limit around 1,300 interactions.
Solution: Implement a rolling window: only store the most recent 50 interactions in the primary UIC record. Archive older interactions to S3 with a DynamoDB pointer.

Edge Case 3: Concurrent Updates Causing Lost Writes

If a customer somehow reaches two agents simultaneously (e.g., an active chat and an inbound call at the same moment), both conversations ending concurrently will trigger two update_item calls. The list_append operation is not atomic; one update may overwrite the other.
Solution: Use DynamoDB’s conditional ConditionExpression with version attribute optimistic locking. Increment a version counter with each update. If a concurrent write is detected (version mismatch), retry the update.

Official References