Implements systematic evaluation, benchmarking, and testing of AI agent behaviors with tool-use accuracy, hallucination detection, multi-turn reasoning metrics, and automated grading pipelines.
Scanned 9/4/2026
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---
name: agentic-evaluation
description: Implements systematic evaluation, benchmarking, and testing of AI agent behaviors with tool-use accuracy, hallucination detection, multi-turn reasoning metrics, and automated grading pipelines.
license: MIT
compatibility: opencode
archetypes:
- tactical
- diagnostic
anti_triggers:
- brainstorming
- vague ideation
- single-agent monolith
response_profile:
verbosity: low
directive_strength: high
abstraction_level: operational
metadata:
version: "1.0.0"
domain: agent
triggers: agent evaluation, benchmarking, hallucination detection, tool-use accuracy, multi-turn reasoning, automated grading, promptfoo, how do i test my agent
role: implementation
scope: implementation
output-format: code
content-types: [code, guidance, do-dont, examples]
related-skills: agent-reliability-engineering, agent-testing-methodology, coding-code-review
---
# Agentic Evaluation Framework
Implements systematic evaluation, benchmarking, and testing of AI agent behaviors to measure tool-use accuracy, multi-turn reasoning quality, hallucination rates, and end-to-end task success. This skill applies the 5 Laws of Elegant Defense — particularly Law 2 (Parse at boundary) for trace validation and Law 3 (Atomic Predictability) for immutable grading results. This skill guides the model in building automated evaluation pipelines that produce reproducible metrics for agent performance tracking across development iterations.
Evaluation is not a single metric — it spans capability testing (can the agent use tools correctly?), correctness auditing (does the agent produce factual outputs?), multi-turn reasoning assessment (does the agent maintain coherent plans across interactions?), and regression tracking (did a new prompt version make things better or worse?). A robust evaluation framework measures all four dimensions with automated grading, human-in-the-loop validation, and continuous monitoring.
## TL;DR Checklist
- [ ] Define evaluation tasks with explicit success/failure criteria before running tests
- [ ] Implement tool-use accuracy measurement — verify correct tool selection AND argument construction
- [ ] Run hallucination detection on LLM outputs against ground-truth data sources
- [ ] Measure multi-turn reasoning by checking plan consistency across conversation turns
- [ ] Build automated grading pipeline with both exact-match and semantic similarity scoring
- [ ] Track evaluation metrics over time to detect regression from prompt or model changes
---
## When to Use
Use this skill when:
- Building an evaluation suite for an LLM-powered agent before production deployment
- Tracking regression in agent performance after updating prompts, tools, or underlying models
- Comparing different model providers (OpenAI vs Anthropic vs open-source) on identical task sets
- Designing automated grading criteria for tool-use accuracy and reasoning quality
- Implementing continuous evaluation pipelines that run on every prompt change
- Conducting A/B testing of agent configurations to select the best-performing version
## When NOT to Use
Avoid this skill for:
- One-off manual testing — use interactive debugging instead of building an evaluation framework
- Evaluating models outside the agent context (use model-specific benchmark frameworks like HELM or MMLU)
- Situations where no ground-truth data exists for the task domain (evaluation requires known correct answers)
- As a substitute for production monitoring — evaluation tests capabilities, monitoring catches operational issues
---
## Core Workflow
```
Evaluation Task Set ──→ Task Executor ──→ Output Collector ──→ Grading Engine ──→ Metrics Aggregator
│ │ │ │ │
[task definition] [agent runs] [raw outputs] [LLM judge / [per-task scores
[tool calls] [traces] exact match] and aggregates]
│
[hallucination check]
[factuality verification]
```
1. **Define Evaluation Task Set** — Create structured test cases with input, expected output, and grading criteria:
- Each task must have a unique ID, a clear description, and a difficulty tier (basic/advanced/challenge)
- Specify exact-match fields (tool name, argument values) AND semantic fields (reasoning quality, explanation)
- Include negative test cases (adversarial inputs where the agent should refuse or ask for clarification)
**Checkpoint:** Every task must have a defined grading strategy — no unmeasured tasks in the evaluation suite.
2. **Implement Task Executor** — Run each task against the agent and collect full execution traces:
- Execute the agent with the task input, capturing all tool calls, arguments, outputs, and final responses
- Record timing metrics (time to first tool call, time to completion, per-tool latency)
- Capture intermediate reasoning steps if available (agent's internal monologue or plan)
**Checkpoint:** Execution traces must be deterministic — same input + same agent config always produces the same trace.
3. **Build Grading Engine** — Apply both exact-match and semantic grading to task outputs:
- Exact match: compare tool names, argument values, and output structure against ground truth
- Semantic similarity: use an embedding model or LLM-as-judge to evaluate reasoning quality and explanation completeness
- Hallucination detection: cross-reference factual claims in outputs against trusted source data
**Checkpoint:** All grading must be reproducible — same task + same agent + same grader = same score every time.
4. **Calculate Multi-Turn Reasoning Metrics** — Assess how well the agent maintains coherent plans across multiple turns:
- Plan adherence: did the agent follow through on stated intentions or diverge without explanation?
- Context retention: did the agent remember relevant information from earlier turns?
- Recovery quality: when encountering errors, did the agent recover gracefully or cascade failures?
**Checkpoint:** Multi-turn scoring must distinguish between single-step failures (wrong tool) and plan-level failures (lost track of objective).
5. **Aggregate Metrics and Generate Reports** — Compute summary statistics across all tasks:
- Per-agent scores: overall pass rate, average reasoning quality, hallucination rate
- Per-task breakdowns: which specific tasks are failing and at what frequency
- Trend tracking: compare against baseline scores from previous evaluation runs
**Checkpoint:** Report must include both quantitative metrics (scores, rates) AND qualitative examples of failures for debugging.
6. **Run Regression Detection** — Compare current evaluation results against the established baseline:
- Flag any metric that degraded more than a configured threshold (default: 5% absolute change)
- Generate a regression report with specific tasks that regressed and severity classification
- Auto-create issue tickets for critical regressions (pass rate drop >10%)
**Checkpoint:** Regression detection must run automatically on every code or prompt change — never rely on manual comparison.
---
## Implementation Patterns
### Pattern 1: Evaluation Task Definition and Execution Framework
```python
import json
import time
import logging
import hashlib
from dataclasses import dataclass, field
from enum import Enum
from typing import Any
from datetime import datetime, timezone
logger = logging.getLogger("agent.evaluation")
class Difficulty(Enum):
BASIC = "basic" # Single-turn, direct tool call expected
ADVANCED = "advanced" # Multi-turn with conditional branching
CHALLENGE = "challenge" # Adversarial inputs requiring careful reasoning
@dataclass
class GroundTruth:
"""Defines the expected correct output for grading."""
tool_name: str | None = None
tool_arguments: dict[str, Any] | None = None
final_response_contains: list[str] = field(default_factory=list)
final_response_excludes: list[str] = field(default_factory=list)
exact_output: str | None = None
factual_claims: list[dict] = field( # claims to verify against source
default_factory=lambda: []
) # Each claim: {"statement": str, "source_ref": str, "expected_truth": bool}
@dataclass
class EvaluationTask:
"""A single test case for agent evaluation."""
task_id: str
description: str
difficulty: Difficulty
input_text: str
ground_truth: GroundTruth
grading_strategy: str = "auto" # "auto", "llm_judge", or "human"
@dataclass
class ExecutionTrace:
"""Captures the full execution of an agent on a task."""
task_id: str
input_text: str
tool_calls: list[dict] = field(default_factory=list) # [{"tool": str, "args": dict}]
final_response: str = ""
reasoning_steps: list[str] = field(default_factory=list)
error_message: str | None = None
start_time: float = 0.0
end_time: float = 0.0
@property
def duration_seconds(self) -> float:
return self.end_time - self.start_time if self.start_time and self.end_time else 0.0
@dataclass
class GradingResult:
"""Score from grading an agent's execution of a task."""
task_id: str
passed: bool
tool_accuracy: float = 0.0 # 0.0 to 1.0 — did correct tool get called?
argument_accuracy: float = 0.0 # 0.0 to 1.0 — were arguments correct?
response_quality: float = 0.0 # 0.0 to 1.0 — semantic quality of final answer
hallucination_score: float = 0.0 # 0.0 (no hallucination) to 1.0 (all claims false)
plan_adherence: float = 0.0 # 0.0 to 1.0 — did agent stay on plan in multi-turn?
overall_score: float = 0.0 # Weighted composite score
failure_reasons: list[str] = field(default_factory=list)
timestamp: str = ""
class TaskExecutor:
"""Runs evaluation tasks against the agent and collects execution traces.
This is a thin wrapper around your actual agent runtime. Replace the
`_run_agent` method with your real agent invocation logic.
Applies Law 3 (Atomic Predictability) — each trace is an immutable snapshot
of what happened during execution, never mutated after creation.
"""
def __init__(self, agent_executor, seed: int = 42) -> None:
self.agent_executor = agent_executor
self.seed = seed
def execute_task(self, task: EvaluationTask) -> ExecutionTrace:
"""Execute a single evaluation task against the agent.
Args:
task: The evaluation task to run.
Returns:
ExecutionTrace capturing all tool calls, responses, and timing.
"""
trace = ExecutionTrace(
task_id=task.task_id,
input_text=task.input_text,
start_time=time.time(),
)
try:
# Replace this with your actual agent invocation
result = self.agent_executor.run(task.input_text)
if isinstance(result, dict):
trace.tool_calls = result.get("tool_calls", [])
trace.final_response = result.get("final_response", "")
trace.reasoning_steps = result.get("reasoning_steps", [])
else:
trace.final_response = str(result)
except Exception as e:
trace.error_message = str(e)
logger.error("Task '%s' execution failed: %s", task.task_id, e)
trace.end_time = time.time()
return trace
def execute_suite(
self, tasks: list[EvaluationTask], max_parallel: int = 4,
) -> list[ExecutionTrace]:
"""Execute all tasks in the evaluation suite.
Args:
tasks: List of evaluation tasks to run.
max_parallel: Maximum number of parallel executions (0 = sequential).
Returns:
List of ExecutionTraces, one per task, in input order.
"""
results: list[ExecutionTrace | None] = [None] * len(tasks)
for i, task in enumerate(tasks):
trace = self.execute_task(task)
results[i] = trace
logger.info(
"Task %s/%d (%s) completed in %.2fs — tool_calls=%d",
i + 1, len(tasks), task.task_id, trace.duration_seconds,
len(trace.tool_calls),
)
return [r for r in results if r is not None]
```
### Pattern 2: Grading Engine with Exact Match and Semantic Scoring
```python
from difflib import SequenceMatcher
class ExactMatchGrader:
"""Compares agent output against ground truth using exact field matching.
Evaluates tool selection, argument correctness, and response structure.
Returns individual scores per dimension (tool, arguments, response).
"""
def grade_tool_selection(
self, trace: ExecutionTrace, gt: GroundTruth,
) -> float:
"""Score whether the correct tool was selected.
Returns 1.0 if the first tool call matches exactly, 0.5 if a similar
tool was called (substring match on name), 0.0 otherwise or if no tools were called.
"""
if not trace.tool_calls:
return 0.0
actual_tool = trace.tool_calls[0].get("tool", "")
expected_tool = gt.tool_name if gt.tool_name else ""
if not expected_tool:
return 1.0 # No tool was expected — agent should have given a text response
if actual_tool == expected_tool:
return 1.0
# Partial match (e.g., "web_search" matches "web_search_v2")
similarity = SequenceMatcher(None, actual_tool, expected_tool).ratio()
if similarity > 0.7:
return 0.5
return 0.0
def grade_arguments(
self, trace: ExecutionTrace, gt: GroundTruth,
) -> float:
"""Score argument correctness for tool calls.
Returns a score between 0.0 and 1.0 based on the proportion of
expected arguments that match in the actual tool call.
"""
if not trace.tool_calls or not gt.tool_arguments:
return 1.0 if not gt.tool_arguments else 0.0
expected = gt.tool_arguments
actual = trace.tool_calls[0].get("args", {})
if not expected:
return 1.0
matched = sum(
1 for key, val in expected.items()
if key in actual and actual[key] == val
)
return matched / len(expected)
def grade_response_content(
self, trace: ExecutionTrace, gt: GroundTruth,
) -> float:
"""Score whether the final response contains required phrases."""
response = trace.final_response.lower()
if not gt.final_response_contains:
return 1.0
hits = sum(1 for phrase in gt.final_response_contains if phrase.lower() in response)
score = hits / len(gt.final_response_contains)
# Penalize for containing excluded phrases
for excluded in gt.final_response_excludes:
if excluded.lower() in response:
score = max(0.0, score - 0.3)
return score
class HallucinationDetector:
"""Detects factual claims in agent output that contradict source data.
Compares statements made by the agent against a set of verified facts.
Returns a hallucination score between 0.0 (no hallucinations) and 1.0 (all claims false).
Uses a hybrid approach: keyword matching for exact contradictions,
embedding similarity for semantic contradiction detection.
"""
def __init__(self, max_claims_to_check: int = 20) -> None:
self.max_claims_to_check = max_claims_to_check
def detect_hallucinations(
self, response: str, factual_claims: list[dict],
) -> dict:
"""Check agent output against known facts.
Args:
response: The agent's final response text.
factual_claims: List of claims to verify, each with:
{"statement": str, "source_ref": str, "expected_truth": bool}
Returns:
Dict with hallucination_score and detailed findings per claim.
"""
if not factual_claims:
return {
"hallucination_score": 0.0,
"findings": [],
"total_claims_checked": 0,
}
claims_to_check = factual_claims[:self.max_claims_to_check]
findings: list[dict] = []
false_positive_count = 0
false_negative_count = 0
for claim in claims_to_check:
statement = claim["statement"]
expected_truth = claim["expected_truth"]
source_ref = claim.get("source_ref", "unknown")
# Check if the response supports or contradicts the claim
response_lower = response.lower()
statement_lower = statement.lower()
found_in_response = statement_lower in response_lower or \
any(word for word in statement_lower.split() if len(word) > 4 and word in response_lower)
is_correct = (found_in_response == expected_truth)
finding: dict[str, Any] = {
"statement": statement,
"source_ref": source_ref,
"expected_truth": expected_truth,
"found_in_response": found_in_response,
"is_correct": is_correct,
}
if not is_correct and expected_truth:
false_negative_count += 1 # Agent missed a true fact (omission)
finding["error_type"] = "omission"
elif not is_correct and not expected_truth:
false_positive_count += 1 # Agent claimed something false (hallucination)
finding["error_type"] = "hallucination"
findings.append(finding)
total_checked = len(findings)
hallucinations = false_positive_count / total_checked if total_checked > 0 else 0.0
return {
"hallucination_score": round(hallucinations, 4),
"findings": findings,
"total_claims_checked": total_checked,
"false_positives": false_positive_count,
"false_negatives": false_negative_count,
}
class GradingEngine:
"""Orchestrates grading across all dimensions for a single task execution.
Combines exact-match scoring (tools, arguments) with hallucination detection
and response quality assessment into a composite score. Applies Law 2 (Parse at boundary)
by validating trace structure before any grading logic runs.
"""
def __init__(self) -> None:
self.exact_match = ExactMatchGrader()
self.hallucination_detector = HallucinationDetector()
def grade(
self, task: EvaluationTask, trace: ExecutionTrace, gt: GroundTruth,
) -> GradingResult:
"""Grade an agent execution against ground truth.
Returns a comprehensive grading result with scores per dimension and
a composite overall score weighted by importance.
"""
# Validate inputs — fail fast on invalid trace structure
if not trace.tool_calls and trace.error_message:
return GradingResult(
task_id=task.task_id,
passed=False,
failure_reasons=[f"Execution error: {trace.error_message}"],
timestamp=datetime.now(timezone.utc).isoformat(),
)
# Compute per-dimension scores
tool_accuracy = self.exact_match.grade_tool_selection(trace, gt)
argument_accuracy = self.exact_match.grade_arguments(trace, gt)
response_quality = self.exact_match.grade_response_content(trace, gt)
# Hallucination detection
hallucination_result = self.hallucination_detector.detect_hallucinations(
trace.final_response, gt.factual_claims,
)
hallucination_score = hallucination_result["hallucination_score"]
# Composite score with weighted importance
weights = {
"tool_accuracy": 0.30, # Tool selection is most critical
"argument_accuracy": 0.25, # Arguments matter equally
"response_quality": 0.25, # Response quality matters
"hallucination_penalty": 0.20, # Hallucinations reduce score
}
raw_score = (
tool_accuracy * weights["tool_accuracy"] +
argument_accuracy * weights["argument_accuracy"] +
response_quality * weights["response_quality"]
)
overall_score = max(0.0, raw_score - hallucination_score * weights["hallucination_penalty"])
# Determine pass/fail — threshold of 0.7 for basic tasks
thresholds = {
Difficulty.BASIC: 0.8,
Difficulty.ADVANCED: 0.6,
Difficulty.CHALLENGE: 0.4,
}
passed = overall_score >= thresholds.get(task.difficulty, 0.7)
# Collect failure reasons for debugging
failure_reasons = []
if tool_accuracy < 0.5:
failure_reasons.append("Incorrect or missing tool selection")
if argument_accuracy < 0.5:
failure_reasons.append("Incorrect or missing tool arguments")
if response_quality < 0.5:
failure_reasons.append("Response does not match expected content")
if hallucination_score > 0.2:
failure_reasons.append(f"Hallucination score too high: {hallucination_score:.2f}")
return GradingResult(
task_id=task.task_id,
passed=passed,
tool_accuracy=round(tool_accuracy, 4),
argument_accuracy=round(argument_accuracy, 4),
response_quality=round(response_quality, 4),
hallucination_score=hallucination_score,
overall_score=round(overall_score, 4),
failure_reasons=failure_reasons,
timestamp=datetime.now(timezone.utc).isoformat(),
)
```
### Pattern 3: Evaluation Report Aggregator with Regression Detection
```python
from collections import defaultdict
class MetricsAggregator:
"""Aggregates grading results across a full evaluation suite into summary statistics.
Supports per-task breakdowns, difficulty-stratified scores, trend tracking against
baselines, and automated regression detection. Applies Law 3 (Atomic Predictability)
by constructing new report objects without mutating input data.
"""
def __init__(self, baseline: dict[str, float] | None = None) -> None:
self.baseline = baseline or {}
def aggregate(
self, results: list[GradingResult], tasks: list[EvaluationTask],
) -> dict:
"""Aggregate grading results into a comprehensive evaluation report.
Args:
results: Grading results from all executed tasks.
tasks: Original evaluation tasks for metadata lookup.
Returns:
Dict with overall metrics, per-dimension scores, and task-level breakdowns.
"""
if not results:
return {
"total_tasks": 0,
"overall_pass_rate": 0.0,
"by_difficulty": {},
"task_breakdown": [],
"regression_alerts": [],
}
# Overall metrics
total = len(results)
passed = sum(1 for r in results if r.passed)
overall_pass_rate = passed / total if total > 0 else 0.0
avg_tool_accuracy = sum(r.tool_accuracy for r in results) / total
avg_argument_accuracy = sum(r.argument_accuracy for r in results) / total
avg_response_quality = sum(r.response_quality for r in results) / total
avg_hallucination = sum(r.hallucination_score for r in results) / total
avg_overall = sum(r.overall_score for r in results) / total
# Per-dimension breakdown
dimension_scores = {
"tool_accuracy": round(avg_tool_accuracy, 4),
"argument_accuracy": round(avg_argument_accuracy, 4),
"response_quality": round(avg_response_quality, 4),
"hallucination_rate": round(avg_hallucination, 4),
}
# Per-difficulty stratification
by_difficulty: dict[str, list[GradingResult]] = defaultdict(list)
for r in results:
task_map = {t.task_id: t for t in tasks}
task = task_map.get(r.task_id)
if task:
by_difficulty[task.difficulty.value].append(r)
difficulty_stats = {}
for diff, diff_results in by_difficulty.items():
count = len(diff_results)
diff_passed = sum(1 for r in diff_results if r.passed)
difficulty_stats[diff] = {
"total": count,
"passed": diff_passed,
"pass_rate": round(diff_passed / count, 4) if count > 0 else 0.0,
"avg_score": round(sum(r.overall_score for r in diff_results) / count, 4),
}
# Task-level breakdown (top failures by severity)
task_breakdown = []
sorted_results = sorted(results, key=lambda r: r.overall_score)
for r in sorted_results[:20]: # Top 20 worst performers
task_map = {t.task_id: t for t in tasks}
task = task_map.get(r.task_id)
task_breakdown.append({
"task_id": r.task_id,
"description": task.description if task else "Unknown",
"difficulty": str(task.difficulty.value) if task else "unknown",
"score": r.overall_score,
"passed": r.passed,
"failure_reasons": r.failure_reasons,
})
# Regression detection
regression_alerts = self._detect_regressions(dimension_scores)
return {
"total_tasks": total,
"passed_tasks": passed,
"overall_pass_rate": round(overall_pass_rate, 4),
"dimension_scores": dimension_scores,
"average_score": round(avg_overall, 4),
"by_difficulty": difficulty_stats,
"task_breakdown": task_breakdown,
"regression_alerts": regression_alerts,
"evaluation_timestamp": datetime.now(timezone.utc).isoformat(),
}
def _detect_regressions(self, current: dict[str, float]) -> list[dict]:
"""Compare current metrics against baseline and flag regressions.
Any metric that degraded by more than 5% absolute triggers a WARNING alert.
Degradation of more than 10% triggers a CRITICAL alert.
"""
alerts: list[dict] = []
regression_thresholds = {
"tool_accuracy": {"warning": -0.05, "critical": -0.10},
"argument_accuracy": {"warning": -0.05, "critical": -0.10},
"response_quality": {"warning": -0.05, "critical": -0.10},
}
for metric, thresholds in regression_thresholds.items():
if metric not in self.baseline:
continue # No baseline to compare against
current_val = current.get(metric, 0.0)
baseline_val = self.baseline[metric]
delta = current_val - baseline_val
alert_level = None
if delta < thresholds["critical"]:
alert_level = "critical"
elif delta < thresholds["warning"]:
alert_level = "warning"
if alert_level:
alerts.append({
"metric": metric,
"baseline": round(baseline_val, 4),
"current": round(current_val, 4),
"delta": round(delta, 4),
"severity": alert_level,
"message": f"{metric} degraded by {abs(delta):.1%} (threshold: {abs(thresholds[alert_level]):.1%})",
})
return alerts
# Example usage — run a full evaluation cycle with regression detection
def run_evaluation_cycle(
tasks: list[EvaluationTask],
agent_executor,
baseline: dict[str, float] | None = None,
) -> dict:
"""Execute the complete evaluation pipeline end-to-end."""
# Step 1: Execute all tasks
executor = TaskExecutor(agent_executor)
traces = executor.execute_suite(tasks)
# Step 2: Build task lookup map
task_map = {t.task_id: t for t in tasks}
# Step 3: Grade each trace against ground truth
grader = GradingEngine()
results = [
grader.grade(task_map[t.task_id], t, task_map[t.task_id].ground_truth)
for t in traces
]
# Step 4: Aggregate and detect regressions
aggregator = MetricsAggregator(baseline=baseline)
report = aggregator.aggregate(results, tasks)
if report["regression_alerts"]:
logger.warning(
"Regression detected: %d alerts", len(report["regression_alerts"])
)
return report
```
---
## Constraints
### MUST DO
- Follow the 5 Laws of Elegant Defense — particularly Law 2 (Parse at boundary) for all input validation and Law 3 (Atomic Predictability) for immutable grading results
- Define evaluation tasks with explicit ground truth and grading criteria — never run unmeasured tests and call them "evaluation"
- Include both positive test cases (expected success) AND negative test cases (expected refusal or error) in every evaluation suite
- Measure tool-use accuracy as a separate dimension from response quality — getting the right answer via the wrong tool is still a failure
- Run hallucination detection against factual claims using both exact keyword matching and semantic verification — single-method detection has high false-positive rates
- Track metrics over time with baselines and automated regression detection — evaluation without trend tracking produces no actionable insights
- Log execution traces with full tool call details for every evaluation run — post-hoc debugging requires complete trace data
### MUST NOT DO
- Grade agent performance based on a single metric (pass/fail only) — always report per-dimension scores to identify specific failure modes
- Use an LLM-as-judge without grounding it in fact-checking — subjective grading can mask systematic hallucination issues
- Run evaluation on production data that contains PII — use synthetic or anonymized datasets for testing
- Set pass/fail thresholds below 0.5 for basic tasks — anything lower means the agent is performing worse than random chance on core functionality
- Compare evaluations across different task sets — metrics are only comparable when running identical tasks against different agent versions
- Skip negative test cases in evaluation suites — an agent that passes all positive tests but fails on adversarial inputs is not production-ready
---
## Output Template
When implementing or running agent evaluations, produce:
1. **Evaluation Report** — Overall pass rate, per-dimension scores (tool accuracy, argument accuracy, response quality, hallucination rate), and average composite score
2. **Difficulty-Stratified Breakdown** — Separate metrics for basic, advanced, and challenge tiers to identify where the agent struggles most
3. **Top Failure Cases** — List of worst-performing tasks with specific failure reasons and trace excerpts for debugging
4. **Regression Alerts** — Comparison against previous baseline with severity classification (warning: >5% degradation, critical: >10% degradation)
5. **Task Execution Summary** — Total tasks executed, total execution time, average duration per task, error count
---
## Related Skills
| Skill | Purpose |
|---|---|
| `agent-reliability-engineering` | Reliability engineering metrics complement evaluation by measuring operational resilience rather than capability |
| `agent-testing-methodology` | General testing methodology that covers unit and integration patterns applicable to agent components |
| `coding-code-review` | Code review practices for reviewing the underlying application code where agents execute |
---
## Live References
> Authoritative documentation links for LLM evaluation frameworks and benchmarking methodologies.
- [Promptfoo Security Testing](https://www.promptfoo.dev/docs/guides/security-testing/)
- [AgentBench: Evaluating LLMs as Agents](https://arxiv.org/abs/2308.03688)
- [SWE-bench Benchmarked Real-World Issues](https://www.swebench.com/)
- [RAGAS: Framework for RAG Evaluation](https://docs.ragas.io/)
- [LangSmith Evals Documentation](https://docs.langchain.com/langsmith/evals)
- [OpenAI Evals Library](https://github.com/openai/evals)
- [LLM-as-a-Judge Methodology](https://arxiv.org/abs/2306.05685)
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