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Augment (Engine)

Augment is the core execution engine of Mutant. It is responsible for orchestrating the transformation of simple baseline scenarios into complex, challenging behavioral datasets.

The Concept

Instead of just asking an LLM to "generate diverse data," the augment() pipeline systematically applies specific types of cognitive or structural changes (Dimensions) to the input Scenario.

The Augmentation Pipeline

Mutant orchestrates these changes through a robust 6-stage pipeline:

  1. Analyze Behavior: Evaluates the baseline scenario to understand its current traits.
  2. Plan Mutations: Selects which dimensions to apply to maximize diversity without breaking the scenario logic.
  3. Generate Mutations: The LLM executes the planned mutations to produce new scenarios.
  4. Quality Review: Ensures the generated scenarios meet quality and coherence standards.
  5. Deduplicate: Removes redundant scenarios that don't add behavioral diversity.
  6. Analyze Coverage: Generates reports on the dataset's overall diversity landscape.

Example: The Output

When a mutation is applied, the scenario transforms. For instance, an "Urgency" mutation might change a standard support request into a high-stakes, time-sensitive crisis.

The output is represented as a MutationCase:

pip install mutant-ai
# or
uv add mutant-ai

# A MutationCase contains the exact change and rationale
case = dataset.cases[0]

print(f"Dimension: {case.dimension_name}")
# Output: "Emotion.Angry"

print(f"Original: {case.original}")
# Output: "I would like a refund."

print(f"Mutated: {case.mutated}")
# Output: "I am absolutely furious! I need my money back immediately!"