Implementing Agent Desktop Process Mining from Screen Recording Event Sequence Analysis

How-To-Articles
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Implementing Agent Desktop Process Mining from Screen Recording Event Sequence Analysis

What This Guide Covers

This guide details the architectural implementation of process mining capabilities by aggregating and analyzing granular screen recording event sequences from the Genesys Cloud Agent Desktop. You will configure the necessary data pipelines to ingest UI interaction telemetry, map these events to specific business process states, and expose this data for downstream analysis in external BI tools or Genesys CX Insights. The end result is a deterministic, timestamped sequence of agent interactions that reveals process bottlenecks, training gaps, and compliance deviations without relying solely on call recording metadata.

Prerequisites, Roles & Licensing

  • Licensing: Genesys Cloud CX 2 or CX 3 license (required for full access to Advanced Analytics and Screen Recording APIs).
  • Permissions:
    • Analytics > Report > View
    • Analytics > Report > Edit (if creating custom dashboards)
    • Screen Recording > Screen Recording > View
    • Screen Recording > Screen Recording > Edit
    • Integrations > Integration > View (if using outbound webhooks)
  • OAuth Scopes: analytics:view, screenrecording:view, integrations:view
  • External Dependencies: A data warehouse or process mining engine (e.g., Celonis, UiPath Process Mining, or Snowflake) capable of ingesting JSON event streams.

The Implementation Deep-Dive

1. Enabling and Configuring Screen Recording Telemetry

The foundation of process mining via screen recording is not the video file itself, but the structured metadata stream generated by the client-side JavaScript agent. Genesys Cloud captures DOM events, keystrokes (if enabled), and window focus changes. To utilize this for process mining, you must ensure the recording configuration captures the necessary granularity without overwhelming the storage or network bandwidth.

Navigate to Admin > Screen Recording. You must enable Screen Recording globally or for specific user groups. Crucially, you must configure the Recording Profile.

The Trap: Many architects enable “Full Screen Recording” with high frame rates, assuming the video feed is the primary data source for analysis. This is a critical error. Video analysis is computationally expensive, slow to index, and prone to OCR errors. Process mining requires structured data. If you rely on video, you cannot programmatically query “how many agents clicked the ‘Escalate’ button between 10:00 and 11:00 AM.” You must prioritize the Event Stream over the video blob.

Configuration Steps:

  1. Create a new Recording Profile.
  2. Set Capture Mode to Events Only or Low Frame Rate Video + Events. For process mining, Events Only is sufficient and drastically reduces storage costs.
  3. Under Event Filters, ensure DOM Changes, Focus/Blur, and Click events are selected.
  4. Enable Metadata Tagging. This allows you to push custom JSON payloads into the event stream.

Architectural Reasoning: By capturing Events Only, you generate a lightweight JSON log for every significant UI interaction. This log is immediately available via the API. The video file is a secondary artifact for human review. Process mining algorithms (like Inductive Miner) require discrete, timestamped events with clear start/end states. DOM clicks provide these states.

2. Mapping UI Events to Business Process States

Raw DOM events are noisy. A click on div.id="container" is meaningless. You must map these technical events to semantic business process steps. This mapping occurs in two places: within the Genesys Cloud UI (via custom CSS/HTML if using a custom agent app) or, more commonly, in the downstream data pipeline.

If you are using the standard Genesys Agent Desktop, you have limited control over DOM IDs. However, if you are using Genesys Cloud Digital or a Custom Agent App, you can inject semantic data.

Strategy A: Standard Agent Desktop (Heuristic Mapping)
You must identify stable CSS selectors or data attributes that correspond to business actions. For example, the “Save Case” button in Salesforce CRM embedded in the desktop often has a consistent class or ID. You must document these mappings.

Strategy B: Custom Agent App (Explicit Telemetry)
If you build a custom agent app using the Genesys Cloud JavaScript SDK, you can explicitly log process events.

import { ScreenRecordingClient } from '@genesyscloud/screen-recording-client';

const screenRecordingClient = new ScreenRecordingClient({
  // Initialize with appropriate credentials
});

// Log a semantic business event
screenRecordingClient.logEvent({
  type: 'CUSTOM',
  payload: {
    action: 'CASE_ESCALATION_INITIATED',
    entityId: 'case-12345',
    timestamp: Date.now()
  }
});

The Trap: Relying solely on DOM structure for mapping in the Standard Desktop. Genesys Cloud updates the Agent Desktop UI frequently. A CSS selector like .button-primary.save-case may change to .btn-action.save in a patch update, breaking your entire process mining pipeline.

Mitigation: Do not map to visual classes (.btn-blue). Map to semantic data attributes or text content where possible. If using the API to retrieve events, filter by text content or stable container IDs. Alternatively, use the Custom Event capability in the SDK to push explicit process states, bypassing DOM fragility entirely.

3. Ingesting Event Data via the Genesys Cloud API

To perform process mining, you must export the event sequences out of Genesys Cloud into a data warehouse. The native Genesys reports are not sufficient for complex process mining algorithms (like Alpha Miner or Inductive Miner) which require raw event logs in formats like XES (XML Event Stream).

You will use the Screen Recording API to fetch event details.

Endpoint: GET /api/v2/screenrecordings/{screenRecordingId}/events

Request:

GET /api/v2/screenrecordings/{screenRecordingId}/events?eventType=CLICK&limit=100
Authorization: Bearer {access_token}

Response Payload:

{
  "total": 1,
  "offset": 0,
  "pageSize": 100,
  "page": 1,
  "order": [],
  "pageOrder": [],
  "elements": [
    {
      "id": "evt-98765",
      "screenRecordingId": "rec-12345",
      "timestamp": "2023-10-27T14:30:00.000Z",
      "eventType": "CLICK",
      "metadata": {
        "x": 150,
        "y": 200,
        "selector": "#save-case-btn",
        "text": "Save Case"
      }
    }
  ]
}

The Trap: Polling individual screen recording IDs is inefficient and rate-limit prone. Do not build a batch job that iterates through every screenRecordingId and calls the events endpoint. This will hit the 429 Too Many Requests limit rapidly.

Solution: Use the Analytics API to identify relevant recordings first, or use Webhooks if available for real-time ingestion. For historical bulk loads, use the Batch API or Data Connector if your target platform supports it. If building a custom ETL pipeline, use the Screen Recording Search API (GET /api/v2/screenrecordings/search) to filter recordings by date range and user, then fetch events in parallel using a connection pool with backoff logic.

Architectural Reasoning: The event stream is append-only and immutable. You must design your ingestion pipeline to handle idempotency. If a network retry occurs, you do not want duplicate events in your process mining engine, which would distort the process path frequency. Use the id field from the event payload as a unique key in your database.

4. Constructing the Process Log for Mining

Process mining engines require a specific data structure: a set of traces (cases), where each trace is a sequence of events with timestamps and activity names.

You must transform the Genesys Cloud event stream into this format.

Transformation Logic:

  1. Case Identification: Group events by screenRecordingId or, more accurately, by the associated Interaction ID (interactionId). A single interaction may span multiple screen recordings if the agent switches contexts. Use the interactionId from the initial event to group all related events.
  2. Activity Mapping: Map the metadata.selector or metadata.text to a standardized Activity Name.
    • #save-case-btn → Activity: Save Case
    • #escalate-btn → Activity: Escalate Case
  3. Timestamp Alignment: Ensure all timestamps are in UTC and normalized.

Example XES Output Snippet:

<log>
  <trace>
    <string key="concept:name">Interaction-12345</string>
    <event>
      <string key="concept:name">Activity: Open Case</string>
      <date key="time:timestamp">2023-10-27T14:30:00.000Z</date>
    </event>
    <event>
      <string key="concept:name">Activity: Search Knowledge Base</string>
      <date key="time:timestamp">2023-10-27T14:30:05.000Z</date>
    </event>
    <event>
      <string key="concept:name">Activity: Save Case</string>
      <date key="time:timestamp">2023-10-27T14:31:00.000Z</date>
    </event>
  </trace>
</log>

The Trap: Treating every screen recording as a single process instance. In reality, an agent may handle multiple interactions in one session, or one interaction may span multiple sessions (e.g., callback). If you key off screenRecordingId, you will fragment process traces. If you key off agentId, you will merge unrelated processes.

Solution: Key off the interactionId. The Genesys Cloud Screen Recording API links events to the active interaction. Use the interactionId as the primary key for the process trace. If an interaction is paused and resumed, the interactionId remains constant, allowing you to stitch together the full process journey.

5. Integrating with Process Mining Engines

Once the data is transformed, you push it to your process mining engine. Most modern engines (Celonis, UiPath, Minit) support API ingestion or direct database connections.

Integration Pattern:

  1. ETL Job: Runs hourly to fetch new events from Genesys Cloud.
  2. Transformation Layer: Applies the Activity Mapping rules.
  3. Load: Inserts into the Process Mining Engine’s staging table.

Validation: Verify that the process paths make sense. For example, if you see a path Open Case -> Save Case -> Open Case, this may indicate a bug in the UI or a user error. Process mining excels at finding these “variant” paths that deviate from the standard operating procedure.

The Trap: Ignoring the “Noise” in screen events. Agents click accidentally, hover over buttons, or switch tabs. This generates spurious events that clutter the process map.

Solution: Implement a Noise Filter in the transformation layer.

  • Duration Filter: Ignore clicks that occur within 100ms of each other (likely accidental double-clicks or rapid tab switching).
  • Relevance Filter: Only include events that map to known business activities. Ignore clicks on empty spaces or generic container divs.
  • Sequence Validation: If an event occurs that breaks the logical flow (e.g., Save Case before Open Case), flag it for review rather than including it in the standard process model.

Validation, Edge Cases & Troubleshooting

Edge Case 1: The “Ghost” Interaction

The Failure Condition: Your process mining engine shows a process path that includes activities that did not occur in the call recording. For example, the agent appears to “Search Knowledge Base” but the call recording shows silence.
The Root Cause: The agent had the Knowledge Base tab open in the background and clicked on it, but did not actually search or use the information. The screen recording captured the CLICK event, but the business intent was not executed.
The Solution: Correlate screen events with other telemetry. Use the Genesys Cloud API to fetch the interaction transcript. If the agent does not mention the knowledge base article, or if the CRM log does not show a search query, flag this screen event as “Low Confidence” in your process model. Do not discard it, but weight it lower in frequency analysis.

Edge Case 2: The “Split-Brain” Agent

The Failure Condition: The process trace shows disjointed activities. Open Case → Save Case → Open Case → Save Case. The process model appears chaotic.
The Root Cause: The agent is handling two interactions simultaneously (e.g., a callback while on a chat). The screen recording events are interleaved.
The Solution: Ensure your ingestion pipeline correctly parses the interactionId for each event. If the Genesys Cloud client does not explicitly tag the interactionId on every DOM event (which it may not for background tabs), you must infer the active interaction from the Call Control API state. Cross-reference the timestamp of the screen event with the activeInteractionId from the agent’s state API at that exact timestamp.

Edge Case 3: The “DOM Shift”

The Failure Condition: Suddenly, 50% of “Save Case” events disappear from the process map. The process frequency drops inexplicably.
The Root Cause: Genesys Cloud updated the Agent Desktop UI, changing the CSS ID or class of the Save button. Your heuristic mapping rule no longer matches.
The Solution: Implement a Monitoring Alert on the volume of mapped events. If the count of a specific activity (e.g., Activity: Save Case) drops by more than 20% week-over-week, trigger an alert to the engineering team. Additionally, use fuzzy matching in your transformation layer. Instead of matching #save-case-btn exactly, match any button with text containing “Save” and “Case”. This is less precise but more resilient to UI changes.

Official References