Implementing ACD Algorithm Comparison Testing Between Round-Robin and Longest-Idle Methods

How-To-Articles
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Implementing ACD Algorithm Comparison Testing Between Round-Robin and Longest-Idle Methods

What This Guide Covers

This guide details the end-to-end methodology for provisioning isolated routing environments, constructing deterministic traffic-splitting flows, and executing controlled load tests to compare Round-Robin and Longest-Idle distribution behaviors. You will have a validated testing framework that captures agent assignment telemetry, calculates distribution fairness metrics, and isolates algorithmic variance from external performance noise.

Prerequisites, Roles & Licensing

  • Licensing Tier: CX 2 or higher. The routing engine exposes all distribution strategies at this tier. Full Analytics API access requires CX 2. WEM Add-on is optional but recommended if you plan to correlate algorithmic distribution with agent performance metrics.
  • Granular Permissions:
    • Routing > Routing Group > Edit
    • Routing > Skill > Edit
    • Architect > Flow > Edit
    • Analytics > Report > View
    • Event Streams > Event Stream > Edit
  • OAuth Scopes: routing:group:write, routing:skill:write, architect:flow:write, analytics:report:view, eventstreams:stream:write
  • External Dependencies: Synthetic load generator (k6, Locust, or custom SIP/HTTP client), time-series database or statistical engine for variance calculation, isolated sandbox organization to prevent production traffic contamination.

The Implementation Deep-Dive

1. Provisioning Isolated Routing Groups with Targeted Strategies

The routing engine evaluates distribution algorithms at the routing group level. You must provision two distinct routing groups that share identical skill configurations and membership pools to ensure the only variable in the experiment is the routingStrategy enum.

Construct the routing groups via the REST API to guarantee exact configuration parity. Manual UI creation introduces subtle metadata differences that skew distribution mathematics.

HTTP Method: POST
Endpoint: https://{your-subdomain}.mypurecloud.com/api/v2/routing/groups
JSON Payload:

{
  "name": "ACD_Test_RoundRobin",
  "description": "Controlled distribution group for Round-Robin algorithm validation",
  "routingStrategy": "ROUND_ROBIN",
  "members": [
    {
      "userId": "agent-id-001",
      "skillLevels": [
        {
          "skillId": "skill-id-general",
          "skillLevel": 1
        }
      ]
    },
    {
      "userId": "agent-id-002",
      "skillLevels": [
        {
          "skillId": "skill-id-general",
          "skillLevel": 1
        }
      ]
    }
  ],
  "wrapUpCodes": [],
  "maxConcurrentInteractions": 1
}

Duplicate this payload for the second group, changing name to ACD_Test_LongestIdle and routingStrategy to LONGEST_IDLE. Maintain identical userId arrays and skillLevels. Set maxConcurrentInteractions to 1 to eliminate concurrent session weighting from the evaluation matrix.

The Trap: Assigning overlapping skills with different skillLevel values across the two groups. The routing engine calculates weighted distribution based on skill proficiency. If Agent A has a skillLevel of 5 in the Round-Robin group and 3 in the Longest-Idle group, the engine will artificially favor Agent A in the Round-Robin pool, destroying your baseline comparison. Skill levels must be identical, or you must disable skill weighting by setting all levels to 1.

Architectural Reasoning: We isolate routing groups instead of queues because queues introduce secondary routing layers. Queue configuration includes waitStrategy, overflow, priority, and outboundCampaign settings that interact with the underlying ACD logic. Routing groups expose the pure distribution algorithm without queue-level mediation. This separation ensures your telemetry reflects algorithmic behavior, not queue optimization heuristics.

2. Architect Flow Construction for Deterministic Traffic Splitting

You require a deterministic traffic router that directs interactions to the appropriate routing group based on a test parameter. The flow must avoid conditional branching that introduces latency or stateful evaluation overhead.

Construct a single Architect flow with the following topology:

  1. Start block configured for your test channel (Voice, Chat, or Email)
  2. Set block that maps an incoming parameter to a routing group identifier
  3. Transfer to Routing Group block that consumes the dynamic identifier

In the Set block, create a data set named targetRoutingGroupId with the expression:

if($in.testGroup == "RR", "routing-group-id-rr", "routing-group-id-li")

Configure the Transfer to Routing Group block to use the targetRoutingGroupId data set as the routing group selector. Disable all queue-level fallbacks. Set the Transfer Mode to Direct.

The Trap: Using a Queue block with dynamic queue selection instead of a Routing Group block. Queue blocks enforce wait strategies and apply queue-level routing overrides. When you pass traffic through a queue, the engine evaluates queue wait time, agent availability, and skill matching before applying the underlying routing strategy. This introduces a secondary evaluation loop that masks the true distribution behavior of Round-Robin versus Longest-Idle. Direct routing group transfer bypasses queue mediation entirely.

Architectural Reasoning: Deterministic traffic splitting requires zero-state evaluation. The Set block executes in memory without database calls. By mapping the routing group ID at flow entry, you eliminate conditional evaluation latency during peak load. The routing engine receives the interaction with a pre-resolved group identifier, allowing the distribution algorithm to execute immediately upon interaction arrival. This architecture mirrors production traffic patterns where routing decisions are pre-calculated by upstream systems.

3. Synthetic Load Generation and Telemetry Ingestion

You must generate controlled interaction volume that simulates realistic arrival patterns while maintaining precise correlation tracking. Randomized load generation introduces Poisson distribution variance that complicates algorithmic comparison. Use fixed-interval generation with deterministic jitter.

Deploy a synthetic client that initiates interactions at 2-second intervals. Each interaction must carry a unique correlation identifier and the testGroup parameter. For voice testing, use SIP INVITE with custom headers. For digital channels, use the Web Chat SDK or REST API with query parameters.

HTTP Method: POST
Endpoint: https://{your-subdomain}.mypurecloud.com/api/v2/conversations/webchat
JSON Payload:

{
  "label": "Synthetic_Test_Payload",
  "participant": {
    "name": "LoadGenerator",
    "email": "test@synthetic.local",
    "address": "synthetic-client-001",
    "customAttributes": {
      "testGroup": "RR",
      "correlationId": "corr-1715423891-001"
    }
  }
}

Simultaneously, provision an Event Stream subscription to capture assignment telemetry. Event Streams provide real-time routing events without the aggregation latency of the Analytics API.

HTTP Method: POST
Endpoint: https://{your-subdomain}.mypurecloud.com/api/v2/eventstreams
JSON Payload:

{
  "name": "ACD_Distribution_Telemetry",
  "description": "Real-time capture of routing assignment events for algorithm comparison",
  "eventTypes": [
    "routing.interaction.assigned"
  ],
  "filters": [
    {
      "type": "routing.interaction.assigned",
      "filter": "routingGroupId IN ('routing-group-id-rr', 'routing-group-id-li')"
    }
  ],
  "deliveryMethod": "firehose",
  "deliveryConfig": {
    "firehoseConfig": {
      "roleArn": "arn:aws:iam::123456789:role/genesys-eventstream-role",
      "destinationStreamArn": "arn:aws:kinesis:us-east-1:123456789:stream/acd-telemetry"
    }
  },
  "eventVersion": "2"
}

Route the Event Stream output to a processing pipeline that extracts routingGroupId, userId, timestamp, and correlationId. Store these records in a time-series database for statistical analysis.

The Trap: Generating load faster than the routing engine processes assignment events. The Genesys Cloud routing engine enforces rate limits and internal queuing thresholds. If your synthetic client exceeds 50 interactions per second per organization, the engine queues requests at the API gateway layer. This artificial queuing inflates interaction arrival timestamps, causing the Longest-Idle algorithm to miscalculate idle windows. Longest-Idle evaluates time since the last interaction disposition. Artificial arrival spikes reset idle timers simultaneously across agents, creating false distribution clustering.

Architectural Reasoning: We use Event Streams instead of polling the Analytics API because assignment events require millisecond precision. The Analytics API aggregates data in 5-minute intervals by default. Algorithmic distribution comparison requires per-assignment granularity to calculate coefficient of variation and distribution skew. Event Streams deliver raw assignment records with sub-second timestamps, enabling precise idle-window calculation. The Firehose delivery method provides exactly-once semantics and backpressure handling, preventing telemetry loss during load spikes.

4. Metric Extraction and Statistical Validation

After executing the load test for a minimum duration of 30 minutes, extract assignment records and calculate distribution fairness metrics. You must evaluate three statistical dimensions: assignment count variance, idle-time utilization efficiency, and handle-time distribution alignment.

Query the stored telemetry data using the following analytical framework:

  1. Assignment Count Variance: Calculate the standard deviation of assignments per agent within each routing group. Round-Robin should produce near-zero variance under uniform handle times. Longest-Idle will show controlled variance based on actual idle windows.
  2. Coefficient of Variation (CV): Compute CV = (Standard Deviation / Mean) * 100. A CV below 15% indicates stable distribution. Values above 25% signal algorithmic skew or configuration drift.
  3. Idle Window Alignment: For Longest-Idle, calculate the difference between the assigned agent’s idle timestamp and the maximum idle timestamp in the pool. This measures how closely the algorithm adheres to true longest-idle selection versus approximate matching.

Execute the Analytics API to validate Event Stream data against aggregated reports. This cross-validation confirms telemetry integrity.

HTTP Method: POST
Endpoint: https://{your-subdomain}.mypurecloud.com/api/v2/analytics/conversations/queries
JSON Payload:

{
  "dateFrom": "2024-01-15T08:00:00.000Z",
  "dateTo": "2024-01-15T09:00:00.000Z",
  "type": "summary",
  "interval": "PT15M",
  "groupBy": [
    "routingGroup.id",
    "routingStrategy",
    "agent.id"
  ],
  "metrics": [
    "interactions.handled",
    "handleTime",
    "wrapTime"
  ],
  "filter": {
    "type": "and",
    "clauses": [
      {
        "type": "equals",
        "dimension": "routingGroup.id",
        "value": ["routing-group-id-rr", "routing-group-id-li"]
      }
    ]
  }
}

Process the response to generate distribution heatmaps. Compare the Round-Robin group against the Longest-Idle group using identical time windows. Document variance deltas and idle-window alignment scores.

The Trap: Analyzing raw assignment counts without normalizing for agent availability windows. Agents logging in at different times or taking breaks create artificial availability skew. Round-Robin cycles through available agents regardless of idle duration. Longest-Idle prioritizes agents who have been available the longest. If Agent A logs in 10 minutes before Agent B, Longest-Idle will assign 60% of interactions to Agent A until the idle window equalizes. You must filter telemetry to include only interactions that occurred when all agents were simultaneously available and active.

Architectural Reasoning: Statistical validation requires normalization against availability state. The routing engine only evaluates agents in Available status. By correlating assignment timestamps with agent state transitions from the agent.state.changed event stream, you isolate pure algorithmic behavior from availability artifacts. This approach mirrors production WFM integration patterns where shift start times and break schedules dictate distribution baselines. Cross-referencing with WFM schedule data ensures your comparison reflects real-world routing conditions rather than synthetic availability artifacts.

Validation, Edge Cases & Troubleshooting

Edge Case 1: Skill Level Weighting Skew

The Failure Condition: Round-Robin distribution shows significant assignment imbalance despite uniform load generation. Longest-Idle distribution appears stable but consistently favors a subset of agents.
The Root Cause: Skill level weighting is enabled at the organization level, and agents possess varying proficiency scores across shared skills. The routing engine applies skill weighting as a multiplier to distribution probability. Round-Robin does not ignore skill weighting; it cycles through agents but applies weighted probability during selection. Longest-Idle compounds this by prioritizing agents with higher skill levels when idle windows are similar.
The Solution: Disable skill weighting for the test period by setting all agent skill levels to 1 in both routing groups. Alternatively, isolate the test to a single skill with uniform proficiency across all agents. Verify the routing:group:write permission includes skill level modification capabilities. Run a pre-test validation query to confirm all agents share identical skill configurations.

Edge Case 2: Wrap-Up Time Distortion in Longest-Idle Calculations

The Failure Condition: Longest-Idle algorithm consistently selects the same agent immediately after disposition, creating a sequential assignment pattern that mimics Round-Robin behavior.
The Root Cause: Wrap-up time configuration resets the idle timer differently than expected. When an agent enters wrap-up, the routing engine marks the agent as Unavailable. The idle timer pauses during wrap-up. Upon returning to Available, the idle timer starts from zero. If wrap-up durations vary across agents, the idle window calculation becomes asynchronous. Agents with shorter wrap-up times return to available state faster, accumulating idle time ahead of peers, which triggers repeated Longest-Idle selection.
The Solution: Standardize wrap-up codes and durations across all test agents. Configure identical mandatory wrap-up periods in the routing group settings. Monitor the routing.interaction.dispositioned event to verify wrap-up completion timestamps. Adjust the load test interval to exceed the maximum wrap-up duration, ensuring all agents return to available state before the next assignment window opens.

Edge Case 3: API Rate Limiting During High-Frequency Telemetry Polling

The Failure Condition: Event Stream delivery drops records, or Analytics API queries return incomplete time-series data. Distribution metrics show artificial gaps that correlate with load test intervals.
The Root Cause: The Genesys Cloud platform enforces API rate limits based on organization tier and request patterns. High-frequency synthetic load generation combined with real-time telemetry polling triggers rate limit counters. Event Streams use Amazon Kinesis Firehose, which enforces its own throughput limits. When request volume exceeds provisioned shards, records are buffered and delivered with latency, breaking timestamp correlation.
The Solution: Implement cursor-based pagination for Analytics API queries. Use the dateFrom and dateTo parameters with 5-minute intervals to stay within rate limit thresholds. Configure Event Streams with multiple Firehose shards to distribute throughput. Add exponential backoff logic to your telemetry ingestion pipeline. Monitor the x-ratelimit-remaining header in API responses and throttle synthetic load generation when counters drop below 20%. Cross-reference telemetry ingestion logs with load test timestamps to identify and discard delayed records.

Official References