> ## Documentation Index > Fetch the complete documentation index at: https://docs.arklex.ai/llms.txt > Use this file to discover all available pages before exploring further. # Evaluation > Use ArkSim to score your agent's responses on helpfulness, coherence, relevance, faithfulness, and goal completion, 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. Evaluation *** ## 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: `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. *** ## Behavior Failure Detection Beyond numeric scores, evaluation detects the type of failure in each underperforming turn: | Failure type | Description | | ---------------------------------- | ------------------------------------------------------------------------- | | `lack of specific information` | Correct but incomplete answer that omits details present in the knowledge | | `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` | Directly contradicts what is stated in the knowledge | | `no failure` | No failure detected | Across all conversations, duplicate failures are deduplicated into a **unique errors** list with occurrence counts. When errors are found and a scenario file is available, the evaluator automatically generates **focus files** for targeted reruns (see [Focus Files](#focus-files) below). *** ## 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 theme={null} # 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: 50 # 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 # Threshold gates (optional — omit or set to null to disable) numeric_thresholds: overall_score: 0.7 faithfulness: 3.5 helpfulness: 3.0 goal_completion: 0.8 qualitative_failure_labels: agent_behavior_failure: ["false information", "disobey user request"] prohibited_statements: ["violated"] ``` 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. 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`. 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`. ### Scope Every custom metric has a **scope** that controls when it runs: | Scope | Runs | Receives | | ------------------ | -------------------------- | -------------------------------------------------------- | | `"turn"` (default) | Once per agent response | The current turn + conversation history up to that point | | `"conversation"` | Once per full conversation | The complete conversation history | Use `"turn"` for response-quality checks (clarity, tone, compliance statements). Use `"conversation"` for end-to-end assessments that only make sense after the whole conversation (goal completion, needs assessment, product suitability). ### Metric Types 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 theme={null} from arksim.evaluator import ( QuantitativeMetric, QuantResult, ScoreInput, ) # Turn-level: scored on every agent response class ClarityScore(QuantitativeMetric): def __init__(self, llm=None): super().__init__(name="clarity", score_range=(0, 5), llm=llm, scope="turn") def score(self, score_input: ScoreInput) -> QuantResult: # self.llm is the evaluator's configured LLM, injected automatically. # score_input.chat_history, .current_turn, .user_goal, .knowledge, etc. value = ... # your scoring logic using self.llm or any other logic return QuantResult(name="clarity", value=value, reason="...") # Conversation-level: scored once after the full conversation class ProductSuitabilityScore(QuantitativeMetric): def __init__(self, llm=None): super().__init__(name="product_suitability", score_range=(0, 5), llm=llm, scope="conversation") def score(self, score_input: ScoreInput) -> QuantResult: # score_input.chat_history contains the full conversation. value = ... return QuantResult(name="product_suitability", value=value, reason="...") ``` 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 theme={null} from arksim.evaluator import ( QualitativeMetric, QualResult, ScoreInput, ) # Turn-level: checked on every agent response class ProhibitedStatements(QualitativeMetric): def __init__(self, llm=None): super().__init__(name="prohibited_statements", llm=llm, scope="turn") def evaluate(self, score_input: ScoreInput) -> QualResult: # self.llm is the evaluator's configured LLM, injected automatically. # score_input.chat_history, .current_turn, .user_goal, .knowledge, etc. label = ... # "ok" | "violated" return QualResult(name="prohibited_statements", value=label, reason="...") # Conversation-level: checked once after the full conversation class NeedsAssessment(QualitativeMetric): def __init__(self, llm=None): super().__init__(name="needs_assessment", llm=llm, scope="conversation") def evaluate(self, score_input: ScoreInput) -> QualResult: # score_input.chat_history contains the full conversation. label = ... # "complete" | "partial" | "missing" return QualResult(name="needs_assessment", value=label, reason="...") ``` ### Available fields Both metric types receive a `ScoreInput`: ```python theme={null} 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 ``` *** ## Threshold Gates Threshold gates let you fail the CLI with a non-zero exit code when evaluation results fall below acceptable levels. All three gate types are evaluated after every `arksim evaluate` or `arksim simulate-evaluate` run. ### `numeric_thresholds` Per-metric minimum scores on each metric's native scale. The mean score across all turns per conversation is compared against the threshold. Any conversation that falls below the threshold fails the run with exit code 1. | Metric | Scale | Notes | | -------------------------------------------------------------------- | ---------- | -------------------------------------------------------- | | `overall_score` | 0–1 | Per-conversation `overall_agent_score` compared directly | | `faithfulness`, `helpfulness`, `coherence`, `verbosity`, `relevance` | 1–5 | Mean of per-turn scores | | `goal_completion` | 0–1 | Per-conversation score compared directly | | Custom quantitative metrics (`scope="turn"`) | Your scale | Mean of per-turn scores | | Custom quantitative metrics (`scope="conversation"`) | Your scale | Per-conversation score compared directly | ```yaml theme={null} numeric_thresholds: overall_score: 0.7 faithfulness: 3.5 helpfulness: 3.0 goal_completion: 0.8 ``` ### `qualitative_failure_labels` Hard-gate failure labels for qualitative metrics. Any evaluated turn whose label appears in the list fails the run with exit code 1. Turns where the metric did not run are skipped. ```yaml theme={null} qualitative_failure_labels: agent_behavior_failure: ["false information", "disobey user request"] prohibited_statements: ["violated"] ``` If any gate fails, the CLI exits with code `1` and logs which conversations and turns did not meet the requirement. See [Exit codes](./schema-reference#exit-codes) for the full list of exit codes. ### Programmatic threshold gates When running ArkSim programmatically (e.g. with a custom agent class), you can apply the same gates by importing `check_numeric_thresholds` and `check_qualitative_failure_labels` directly from `arksim.evaluator`: ```python theme={null} import sys from arksim.evaluator import ( Evaluator, check_numeric_thresholds, check_qualitative_failure_labels, ) evaluator_output = evaluator.evaluate(simulation_output) numeric_ok = check_numeric_thresholds( evaluator_output, numeric_thresholds={ "clarity": 3.5, "goal_completion": 0.6, }, ) qual_ok = check_qualitative_failure_labels( evaluator_output, qualitative_failure_labels={ "agent_behavior_failure": ["false information"], "prohibited_statements": ["violated"], }, ) if not numeric_ok or not qual_ok: sys.exit(1) ``` You can also write your own custom gate by inspecting `evaluator_output.conversations` directly and applying any logic you need: ```python theme={null} # Example: fail if any conversation has overall_agent_score below 0.7 for convo in evaluator_output.conversations: if convo.overall_agent_score < 0.7: print(f"Conversation {convo.conversation_id} failed: score {convo.overall_agent_score:.2f}") sys.exit(1) ``` *** ## Running Evaluation ```bash theme={null} pip install arksim ``` ```bash theme={null} arksim evaluate config.yaml ``` ```python theme={null} from arksim.evaluator import run_evaluation, EvaluationInput evaluation_results = run_evaluation(EvaluationInput( simulation_file_path="./simulation.json", scenario_file_path="./scenarios.json", output_dir="./evaluation", model="gpt-5.1", provider="openai", num_workers=50, generate_html_report=True, )) ``` ```python theme={null} from arksim.scenario import Scenarios from arksim.evaluator import run_evaluation, EvaluationInput # simulation comes from run_simulation(...) scenarios = Scenarios.load("./scenarios.json") evaluation_results = run_evaluation(EvaluationInput( output_dir="./evaluation", model="gpt-5.1", provider="openai", num_workers="auto", generate_html_report=True, ), simulation=simulation, scenarios=scenarios) ``` *** ## 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. | | `focus/error_N.json` | Filtered scenario file for error group N (sorted by severity). Only appears when unique errors are detected and a scenario file is provided. See [Focus Files](#focus-files). | | `focus/all_failures.json` | Union of all failing scenarios across every error group. Use this to rerun everything that failed in one pass. | *** ## Reading the Output Results go to `evaluation.json`: **conversations** (one per run), **unique\_errors** (deduplicated failures), and **error\_scenario\_mappings** (which scenarios triggered each error). 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. 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. Each **`error_scenario_mappings[]`** entry maps one unique error to the scenarios that triggered it: * **`unique_error_id`**: Links to the matching entry in `unique_errors`. * **`scenario_ids`**: Which scenarios triggered this error. Use these IDs with `--scenario_file_path` to rerun only the affected scenarios. * **`severity`** and **`error_description`**: Same as the linked `unique_errors` entry, included for convenience. This field is empty when no errors are detected or no scenario file was provided. See [Focus Files](#focus-files) for the file-based workflow. *** ## Focus Files When evaluation detects unique errors and a scenario file is provided, the evaluator generates **focus files** in a `focus/` subdirectory. These are filtered scenario files that let you rerun only the failing subset without any manual config. ### How it works Each unique error group is mapped back to the scenarios that triggered it. The evaluator writes one JSON file per error group plus a combined file with all failing scenarios: ``` evaluation/ evaluation.json final_report.html focus/ error_1.json # scenarios for the highest-severity error error_2.json # scenarios for the second error all_failures.json # union of all failing scenarios ``` Error files are numbered by severity (critical first, then high, medium, low) and by occurrence count within the same severity level. Each focus file uses the standard [Scenarios schema](./schema-reference#scenario-input-scenariosjson), so it works directly with `--scenario_file_path`: ```bash theme={null} # Rerun only the scenarios that triggered error group 1 arksim simulate-evaluate config.yaml \ --scenario_file_path ./evaluation/focus/error_1.json # Or rerun all failing scenarios at once arksim simulate-evaluate config.yaml \ --scenario_file_path ./evaluation/focus/all_failures.json ``` Pass a separate `--output_dir` for each rerun so results do not overwrite the original evaluation. Each rerun produces its own evaluation, report, and focus files (or no focus files if everything passes). ### Iterative fix workflow Focus files support an iterative fix-and-verify loop: 1. Run `arksim simulate-evaluate config.yaml` and review the errors 2. Fix the highest-severity bug in your agent 3. Rerun with the focus file: `--scenario_file_path ./evaluation/focus/error_1.json --output_dir ./results/rerun_1` 4. If the error is resolved, move to the next error group 5. When all targeted reruns pass, do a full battle test with the original scenario file The focus set shrinks as you fix bugs. Scenarios that pass on rerun will not appear in the new run's focus files.