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Now that you have an agent working, you need to write tests to ensure that the quality of your answers don’t degrade as you add additional context. To add a test to your agent, you can add the following to your .agent.yml file. 
tests:
  - type: consistency
    n: 10
    task_description: "how many nights did I get high quality sleep?"
You can add as many tests as you’d like, for as many prompts as you like. For example: 
tests:
  - type: consistency
    n: 10
    task_description: "how many nights did I get high quality sleep?"
  - type: consistency
    n: 10
    task_description: "how many hours do I sleep on average?"
  - type: consistency
    n: 10
    task_description: "what day do I typically get the most sleep?"
You can then run these tests using the following command: 
oxy test my-agent.agent.yml
This will generate a final accuracy score and surface any consistency errors that the LLM detects.

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Understanding Consistency Tests

Since Oxy is built for data analysis, consistency tests are optimized for numerical data and analytical insights. The evaluator intelligently handles common data analysis scenarios:

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What Gets Ignored (Not Considered Errors)

Numerical Rounding (< 0.1% difference):
  • $1,081,396 vs $1,081,395.67 ✅ Consistent
  • $1,065,619 vs $1,065,618.90 ✅ Consistent
  • Different database precision settings
  • Rounding from visualization tools
Grammar & Style Variations:
  • "Revenue amounts to $1M" vs "Revenue amount to $1M" ✅ Consistent
  • Different phrasing of the same insight
  • Synonym usage in descriptions
Formatting Differences:
  • Date formats, number formatting, whitespace

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What Actually Fails Tests

Material disagreements like:
  • $500,000 vs $450,000 ❌ (10% difference)
  • "Sales increased" vs "Sales decreased" ❌ (contradictory)
  • Different conclusions or incompatible recommendations
This approach ensures your tests focus on factual correctness while being practical about data analysis realities.
See the default logic: The built-in consistency evaluator uses a detailed prompt optimized for data analysis. You can view the full default prompt to understand exactly how it evaluates consistency.

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Customizing Evaluation Logic

For specific use cases, you can customize how consistency is evaluated by providing a custom prompt: 
tests:
  # Default behavior - uses built-in smart evaluator
  - type: consistency
    n: 10
    task_description: "How many hours do I sleep on average?"

  # Financial data - strict exact matching
  - type: consistency
    n: 10
    task_description: "What is our Q4 revenue?"
    prompt: |
      Financial data requires exact precision.
      Task: {{ task_description }}
      Submission 1: {{ submission_1 }}
      Submission 2: {{ submission_2 }}

      CONSISTENT (A) only if numbers match exactly.
      Answer A or B.

  # Trend analysis - focus on direction, not exact numbers
  - type: consistency
    n: 10
    task_description: "What's the sleep quality trend?"
    prompt: |
      Evaluate if these describe the same overall trend.
      Ignore exact percentages, focus on direction.

      Task: {{ task_description }}
      Submission 1: {{ submission_1 }}
      Submission 2: {{ submission_2 }}

      Answer A (same trend) or B (different trends).
When to customize:
  • Default prompt (recommended): General data analysis, handles rounding intelligently
  • Strict custom prompt: Financial calculations, compliance reports requiring exact values
  • Lenient custom prompt: Trend analysis, qualitative insights, high-level summaries
  • Modified default: Start with the default prompt source and adapt it for your domain
Example: Adapting the default prompt You can copy the default CONSISTENCY_PROMPT and modify specific rules: 
tests:
  - type: consistency
    n: 10
    task_description: "Calculate inventory costs"
    # Custom prompt based on default but with stricter rounding rules
    prompt: |
      You are evaluating if two submissions are FACTUALLY CONSISTENT for inventory analysis.

      **MANDATORY OVERRIDE RULES - READ THIS FIRST:**

      If you see ANY of these, you MUST answer A immediately:
      ✓ Rounding difference < $0.10 (stricter than default $1) → IMMEDIATELY Answer: A
      ✓ One submission includes additional details the other lacks → IMMEDIATELY Answer: A
      ✓ Grammar/style/formatting differences only → IMMEDIATELY Answer: A

      [BEGIN DATA]
      ************
      [Question]: {{ task_description }}
      ************
      [Submission 1]: {{ submission_1 }}
      ************
      [Submission 2]: {{ submission_2 }}
      ************
      [END DATA]

      ## EVALUATION RULES

      ### ALWAYS CONSISTENT (Answer: A)

      1. **Rounding differences < $0.10** (stricter for inventory)
         * Any difference under 10 cents → A

      2. **Additional Details**
         * One submission has more context → A
         * "Doesn't mention X" is NOT the same as "Contradicts X"

      ### ONLY INCONSISTENT (Answer: B) when:
      * Different item counts or SKUs
      * Material numerical difference (> $1)
      * Contradictory inventory status

      Now evaluate. Answer A (consistent) or B (inconsistent).

      Reasoning:

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Advanced Testing Options

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CI/CD Integration

For automated testing in CI/CD pipelines, use the JSON output format: 
oxy test my-agent.agent.yml --format json
This outputs machine-readable JSON like {"accuracy": 0.855} that can be parsed by your CI tools.

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Quality Gates

Enforce minimum accuracy thresholds to prevent regressions: 
# Fail the build if accuracy drops below 80%
oxy test my-agent.agent.yml --format json --min-accuracy 0.8
The command will exit with code 1 if the threshold isn’t met, making it perfect for CI quality gates.

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Multiple Test Management

If you have multiple tests in your agent file, control how thresholds are evaluated: 
# Average mode: average of all tests must meet threshold (default)
oxy test my-agent.agent.yml --min-accuracy 0.8 --threshold-mode average

# All mode: every individual test must meet threshold
oxy test my-agent.agent.yml --min-accuracy 0.8 --threshold-mode all
For complete documentation on testing features, see the Testing Guide. At this point, you have a working agent as well as the ability to modify and test this agent. Congratulations!