---
title: "Evaluation"
description: "Score your agent's responses across every conversation turn and surface failure patterns with actionable fixes."
---

## What is Evaluation?

Evaluation analyzes the conversation transcripts produced by simulation and scores your agent across multiple dimensions.

The **output** is a set of files summarizing your agent's performance across turns, conversations, and error types.

<Frame>
  ![Evaluation](/images/evaluation.svg)
</Frame>

---

## What Gets Evaluated

Each agent response is scored on five metrics per turn:

| Metric           | What it measures                                       | Scale |
| ---------------- | ------------------------------------------------------ | ----- |
| **Helpfulness**  | How effectively the response addresses the user's need | 1–5   |
| **Coherence**    | Logical flow and clarity of the response               | 1–5   |
| **Relevance**    | How directly the response addresses the user's input   | 1–5   |
| **Verbosity**    | Conciseness; penalizes unnecessarily long responses.   | 1–5   |
| **Faithfulness** | Whether the response contradicts the user's knowledge  | 1–5   |

Scores range from **1 (poor)** to **5 (excellent)**, with 3–4 considered good and 4–5 excellent. You can add domain-specific [custom metrics](#custom-metrics) (e.g. product suitability, compliance) via Python files.

At the conversation level, two additional scores are computed on a 0–1 scale:

- **Goal Completion**: whether the user's goal was fully addressed by the end of the conversation.
- **Turn Success Ratio**: proportion of turns with no detected behavior failure.

These combine into an **Overall Agent Score** per conversation:

<Note>
  `overall_agent_score = turn_success_ratio × 0.75 + goal_completion_score × 0.25`

  Each conversation is assigned a status based on this score. For the exact status values and thresholds, see [Evaluation output](/schema-reference#evaluation-output-evaluationjson) in the Schema Reference.
</Note>

---

## Behavior Failure Detection

Beyond numeric scores, evaluation detects the type of failure in each underperforming turn:

| Failure type                       | Description                                              |
| ---------------------------------- | -------------------------------------------------------- |
| `lack of specific information`     | Generic or partial answer that omits important details   |
| `failure to ask for clarification` | Proceeds with an assumption instead of asking the user   |
| `disobey user request`             | Completely ignores what the user asked                   |
| `repetition`                       | Restates previous content without adding value           |
| `false information`                | Contradicts/fabricates information relative to knowledge |
| `no failure`                       | No failure detected                                      |

Across all conversations, duplicate failures are deduplicated into a **unique errors** list with occurrence counts.

---

## Inputs

The input to evaluation is the simulation output file (e.g. `simulation.json`) from the [Simulation](/simulate-conversation) step. Knowledge referenced during evaluation comes from your [Scenarios](/build-scenario) file.

Evaluation is configured via a YAML file:

```yaml
# Inputs
scenario_file_path: ./examples/path/to/scenario.json
simulation_file_path: ./examples/path/to/simulation.json

# Model
provider: openai
model: gpt-5.1

# Execution
num_workers: auto
score_threshold: null

# Metrics
metrics_to_run: [
  faithfulness,
  helpfulness,
  coherence,
  verbosity,
  relevance,
  goal_completion,
  agent_behavior_failure
]
custom_metrics_file_paths: []

# Output
output_dir: ./examples/path/to/evaluation
generate_html_report: true
```

For all evaluation config keys, types, and defaults, see [Run configuration → Evaluation keys](/schema-reference#evaluation-keys) in the Schema Reference.

---

## Custom Metrics

In addition to the built-in metrics (helpfulness, coherence, relevance, verbosity, faithfulness, goal completion, behavior failure), you can define **custom metrics** in Python and load them via config.

<ParamField body="custom_metrics_file_paths" type="list[str]">
  List of paths to Python files. Each file is loaded and every public `QuantitativeMetric` or `QualitativeMetric` subclass is instantiated and run. Custom metrics **always run** — they are not filtered by `metrics_to_run`.
</ParamField>

<ParamField body="metrics_to_run" type="list[str]">
  Names of built-in metrics to run. If empty, all built-in metrics run. Use this to restrict evaluation to a subset of built-ins while still running all custom metrics from `custom_metrics_file_paths`.
</ParamField>

### Metric Types

<Tabs>
  <Tab title="Quantitative">
    Produces a **numeric score** (e.g. 0–5).

    | Step      | Detail                                               |
    | --------- | ---------------------------------------------------- |
    | Subclass  | `QuantitativeMetric`                                 |
    | Implement | `score(score_input: ScoreInput) -> QuantResult`      |
    | Return    | `QuantResult` with `name`, `value` (float), `reason` |

    ```python
    from arksim.evaluator import (
        QuantitativeMetric,
        QuantResult,
        ScoreInput,
    )

    class ClarityScore(QuantitativeMetric):
        def score(self, score_input: ScoreInput) -> QuantResult:
            # Use your own LLM or any logic; independent of evaluator model config.
            # score_input.chat_history, .user_goal, .knowledge, etc.
            value = ...  # your scoring logic or LLM call
            return QuantResult(name="clarity", value=value, reason="...")
    ```
  </Tab>
  <Tab title="Qualitative">
    Produces a **categorical label** (e.g. `"complete"` / `"partial"`).

    | Step      | Detail                                            |
    | --------- | ------------------------------------------------- |
    | Subclass  | `QualitativeMetric`                               |
    | Implement | `evaluate(score_input: ScoreInput) -> QualResult` |
    | Return    | `QualResult` with `name`, `value` (str), `reason` |

    ```python
    from arksim.evaluator import (
        QualitativeMetric,
        QualResult,
        ScoreInput,
    )

    class NeedsAssessment(QualitativeMetric):
        def evaluate(self, score_input: ScoreInput) -> QualResult:
            # Use score_input.chat_history, .user_goal, .knowledge, etc.
            label = ...  # "complete" | "partial" | "missing"
            return QualResult(name="needs_assessment", value=label, reason="...")
    ```
  </Tab>
</Tabs>

### Available fields

Both metric types receive a `ScoreInput`:

```python
score_input.chat_history   # Full conversation turns
score_input.user_goal      # The goal the user was trying to achieve
score_input.knowledge      # Retrieved knowledge / context passed to the agent
```

---

## Running Evaluation

<Steps>
  <Step title="Install Arksim">
    ```bash
    pip install arksim
    ```
  </Step>
  <Step title="Run evaluation">
    <Tabs>
      <Tab title="CLI">
        ```bash
        arksim evaluate config.yaml
        ```
      </Tab>
      <Tab title="Python (from file)">
        ```python
        import arksim

        evaluation_results = arksim.evaluate(
            simulation_file_path="./simulation.json",
            output_dir="./evaluation",
            model="gpt-5.1",
            provider="open-ai",
            num_workers="auto",
            generate_html_report=True,
        )
        ```
      </Tab>
    </Tabs>
  </Step>
</Steps>

---

## Output Files

| File                | Description                                                                                                                                                                       |
| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `evaluation.json`   | Full evaluation output: conversations with scores, failure labels, and unique errors. See [Evaluation output](/schema-reference#evaluation-output-evaluationjson) for the schema. |
| `final_report.html` | High-level summary with metric averages, overall assessment, and top unique errors, rendered for easier browsing and sharing.                                                      |

---

## Reading the Output

Results go to `evaluation.json`: **conversations** (one per run) and **unique_errors** (deduplicated failures).

<Expandable title="By conversation">
  Each `conversations[]` entry has:

  - **`overall_agent_score`** (0–1): Turn success + goal completion. Use to rank or filter.
  - **`evaluation_status`**: `Done`, `Partial Failure`, `Failed`, or `Evaluation Failed`.
  - **`goal_completion_score`** (0–1, or -1 if skipped): How fully the goal was addressed. **`goal_completion_reason`** explains it.
  - **`turn_success_ratio`** (0–1): Share of turns with no behavior failure.

  Use **`turn_scores[]`** for a turn: `turn_id`, **`scores`** (e.g. helpfulness, faithfulness), **`turn_behavior_failure`**, **`turn_behavior_failure_reason`**, **`unique_error_ids`**. Match IDs to **`unique_errors`** for category and description.
</Expandable>

<Expandable title="By error type">
  Each **`unique_errors[]`** entry has:

  - **`unique_error_description`**: What went wrong.
  - **`behavior_failure_category`**: e.g. `false information`, `failure to ask for clarification`, `repetition`.
  - **`severity`**: e.g. `critical`, `high`, `medium`, `low`.
  - **`occurrences`**: `{ "conversation_id", "turn_id" }` list. Use to find the conversation and turn in logs or CSV.

  Use **`occurrences`** to go from an error to the exact turn; then check **`turn_scores`** and the transcript.
</Expandable>
