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Metric Chaining allows you to create conditional metric flows where a follow-up metric runs only when specific criteria are met by a trigger metric. This approach helps you avoid cramming multiple evaluation checks into a single metric while ensuring accuracy and efficiency in your evaluations. Instead of creating one complex metric that tries to handle multiple scenarios, you can break down your evaluation logic into separate, focused metrics that run conditionally based on the results of previous metrics.

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Benefits of Metric Chaining

  • Improved Accuracy: Each metric focuses on a specific aspect of the conversation
  • Efficiency: Only run metrics that are relevant to the specific conversation flow
  • Clarity: Separate concerns make metrics easier to understand and maintain
  • Flexibility: Create complex evaluation logic without overwhelming single metrics

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How Metric Chaining Works

  1. Trigger Metric: The primary metric that runs first and determines whether additional metrics should execute
  2. Follow-up Metric: The secondary metric that runs conditionally based on the trigger metric’s result
  3. Criteria: The condition that determines when the follow-up metric should run (e.g., “equal to 0”, “greater than 0”)

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TL;DR Walkthrough:

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Example Use Case: Appointment Setter Agent

Consider an appointment setter agent with two distinct evaluation needs:
  1. Repeat Caller Handling: Check if the agent correctly identifies repeat callers
  2. Patient Information Collection: Check if the agent collects necessary information (first name, last name, phone number)
Without metric chaining, you might be tempted to create one large metric covering both scenarios. With metric chaining, you can:
  • Use “Repeat Caller Handling” as your trigger metric
  • If it returns “No” (agent couldn’t identify as repeat caller), then run “Patient Information Collection”
  • If it returns “Yes” (agent identified repeat caller), skip the information collection check

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Setting Up Metric Chaining

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Prerequisites

Before creating a metric chain, ensure you have:
  1. Created your trigger metric
  2. Created your follow-up metric
  3. Tested both metrics individually

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Creating a Metric Chain

  1. Navigate to Metric Chains in your dashboard
  2. Click “Add a Metric Chain”
  3. Configure the chain:
    • Status: Set as Active or Inactive
    • Trigger Metric: Select your primary metric
    • Follow-up Metric: Select the metric to run conditionally
    • Criteria: Define when the follow-up metric should run
      • Equal to 0: Run follow-up when trigger returns “No”
      • Greater than 0: Run follow-up when trigger returns “Yes”
      • Other conditions: As needed for your use case
  4. Save your metric chain

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Applying Metric Chains in Evaluations

When launching an evaluation with metric chains:
  1. In your evaluation setup, select only the trigger metric
  2. The follow-up metric will automatically run based on your chain conditions
  3. Do not manually select the follow-up metric - the chain will handle this
<Note> Only select the trigger metric when launching evaluations. The chained metrics will run automatically based on your configured conditions. </Note>

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Example Results

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Scenario 1: Trigger Metric Returns “Yes”

  • Trigger: “Repeat Caller Handling” returns Yes
  • Result: Agent successfully identified repeat caller
  • Chain Action: Follow-up metric does NOT run
  • Transcript Example: “Perfect, John Doe, I see you…”

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Scenario 2: Trigger Metric Returns “No”

  • Trigger: “Repeat Caller Handling” returns No
  • Result: Agent could not identify repeat caller
  • Chain Action: “Patient Information Collection” runs automatically
  • Transcript Example: “I can’t find your information in our system, I’m sorry…”

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Best Practices

  • Start Simple: Begin with two-metric chains before creating more complex flows
  • Test Individually: Ensure each metric works correctly on its own before chaining
  • Clear Logic: Make sure your chain conditions align with your evaluation goals
  • Document Chains: Keep track of your metric chain logic for team collaboration

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Advanced Usage

Metric chaining can be extended for more complex scenarios:
  • Multi-step Chains: Chain multiple metrics in sequence
  • Different Conditions: Use various threshold conditions for triggering
  • Business Logic: Implement complex business rules through chained evaluations
Need more complex metric chaining scenarios? Contact our team to discuss advanced metric chain configurations for your specific use case.

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Metric Chaining vs Workflow Verification: When to Use Each

Understanding the differences and choosing the right evaluation approach for your use case
Both Metric Chaining and Workflow Verification help you evaluate conditional logic in your agent conversations, but they serve different purposes and work in distinct ways. This guide helps you choose the right approach for your specific evaluation needs.

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Overview Comparison

FeatureMetric ChainingWorkflow Verification
PurposeCustom conditional evaluation logicPre-defined workflow compliance checking
Setup ComplexityModerate (create multiple metrics)Simple (uses existing agent workflow)
FlexibilityHigh - any conditional logicLimited to predefined workflows
GranularitySeparate results for each conditionSingle workflow compliance score
EfficiencyRuns only relevant metricsEvaluates entire workflow path

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When to Use Metric Chaining

Choose Metric Chaining when you need:

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Custom Conditional Logic

  • Complex “if-then” scenarios that don’t follow a linear workflow
  • Multiple branching conditions based on conversation context
  • Business rules that vary based on user characteristics or responses

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Granular Insights

  • Separate scores for each evaluation step
  • Detailed breakdown of where conversations succeed or fail
  • Ability to analyze specific conditional branches independently

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Efficiency Optimization

  • Avoid running irrelevant evaluations
  • Save computation costs on large-scale monitoring
  • Focus evaluation resources on applicable scenarios

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Example Use Cases

  • New vs. Returning Users: “If user is new → check info collection, if returning → check account verification”
  • Product-Specific Flows: “If insurance inquiry → check coverage questions, if claims → check claim validation”
  • Escalation Scenarios: “If technical issue → check troubleshooting steps, if billing → check payment verification”

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When to Use Workflow Verification

Choose Workflow Verification when you have:

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Pre-Defined Linear Workflows

  • Clear, sequential steps your agent should follow
  • Workflows already configured during agent creation
  • Standard operating procedures that rarely change

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Overall Compliance Checking

  • Need to verify agents follow established processes
  • Simple pass/fail evaluation for entire workflow
  • Regulatory or compliance requirements

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Quick Setup Requirements

  • Want immediate evaluation without creating custom metrics
  • Have straightforward, documented agent workflows
  • Need basic workflow adherence monitoring

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Example Use Cases

  • Customer Service Flow: “Greeting → Issue Identification → Resolution → Closure”
  • Sales Process: “Qualification → Needs Assessment → Presentation → Close”
  • Support Tickets: “Intake → Categorization → Assignment → Resolution”

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Detailed Example: Appointment Scheduling Agent

Let’s compare how each approach handles an appointment scheduling scenario:

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Scenario: Agent should collect different information based on appointment type

Requirements:
  • New patient appointments: Collect name, phone, insurance
  • Follow-up appointments: Verify existing info, confirm time
  • Emergency appointments: Prioritize urgency, collect minimal info

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Metric Chaining Approach

Trigger Metric: "Appointment Type Identification"
├── If "New Patient" → Run "New Patient Info Collection"
├── If "Follow-up" → Run "Existing Patient Verification" 
└── If "Emergency" → Run "Emergency Prioritization Check"
Benefits:
  • Each appointment type gets targeted evaluation
  • Separate success rates for different flows
  • No wasted evaluations on irrelevant scenarios
Results Example:
  • Appointment Type ID: 95% success
  • New Patient Info: 87% success (only for new patients)
  • Follow-up Verification: 92% success (only for follow-ups)

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Workflow Verification Approach

Predefined Workflow:
1. Identify appointment type
2. Collect appropriate information
3. Schedule appointment
4. Confirm details
Benefits:
  • Simple setup using existing agent workflow
  • Single compliance score for entire process
  • Easy to understand pass/fail results
Results Example:
  • Overall Workflow Compliance: 89% success

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Implementation Guidance

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Start With Workflow Verification If:

  • Your agent has well-defined, linear workflows
  • You need quick evaluation setup
  • Simple compliance checking meets your needs
  • Your team prefers straightforward metrics