agent-evaluation

Agent Evaluation

Framework for evaluating AI agent outputs: hallucination detection, quality scoring, behavioral regression, and CI/CD integration.

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Hallucination Detection

Fact-Checking Pipeline

def detect_hallucination(response: str, context: str) -> dict:
    """Check if response contains unsupported claims."""
    claims = extract_claims(response)
    supported = [c for c in claims if verify_against(c, context)]
    return {
        'hallucination_rate': 1 - len(supported) / len(claims),
        'unsupported_claims': [c for c in claims if c not in supported]
    }

Red Teaming

• Generate adversarial prompts designed to trigger hallucinations
• Test with contradictory context
• Verify model says "I don't know" when appropriate

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Output Quality Scoring

RAGAS Metrics

Metric	What it measures	Target
Faithfulness	Response grounded in context	≥0.8
Answer Relevance	Response addresses query	≥0.8
Context Precision	Retrieved context is relevant	≥0.7
Context Recall	All relevant context retrieved	≥0.7

Scoring Pipeline

from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevance

result = evaluate(
    dataset=eval_dataset,
    metrics=[faithfulness, answer_relevance]
)

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Behavioral Regression Testing

Test Types

• Consistency: Same input → same output across runs
• Robustness: Slight input variation → similar output
• Boundary: Edge cases handled gracefully
• Safety: No harmful, biased, or leaked content

LangSmith Integration

from langsmith import Client

client = Client()
client.evaluate(
    runnable,
    dataset_name="agent-behavior-tests",
    evaluators=[consistency_evaluator, safety_evaluator]
)

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Latency & Reliability Benchmarks

Metric	Target	Measurement
P50 Latency	<2s	Time to first token
P99 Latency	<10s	End-to-end response time
Error Rate	<1%	Failed requests / total
Throughput	>10 req/s	Concurrent requests handled