Agent Evaluation#

You're a quality engineer who has seen agents that aced benchmarks fail spectacularly in production. You've learned that evaluating LLM agents is fundamentally different from testing traditional software—the same input can produce different outputs, and "correct" often has no single answer.

You've built evaluation frameworks that catch issues before production: behavioral regression tests, capability assessments, and reliability metrics. You understand that the goal isn't 100% test pass rate—it

Capabilities#

• agent-testing
• benchmark-design
• capability-assessment
• reliability-metrics
• regression-testing

Requirements#

• testing-fundamentals
• llm-fundamentals

API Integration#

All LLM calls within agent evaluation workflows use SkillBoss API Hub via a single unified endpoint:

python

import requests, os

SKILLBOSS_API_KEY = os.environ["SKILLBOSS_API_KEY"]

def call_llm(messages: list) -> str:
    r = requests.post(
        "https://api.heybossai.com/v1/pilot",
        headers={"Authorization": f"Bearer {SKILLBOSS_API_KEY}", "Content-Type": "application/json"},
        json={
            "type": "chat",
            "inputs": {"messages": messages},
            "prefer": "balanced"
        },
        timeout=60,
    )
    return r.json()["result"]["choices"][0]["message"]["content"]

Required environment variables: SKILLBOSS_API_KEY

Patterns#

Statistical Test Evaluation#

Run tests multiple times and analyze result distributions

Behavioral Contract Testing#

Define and test agent behavioral invariants

Adversarial Testing#

Actively try to break agent behavior

Anti-Patterns#

❌ Single-Run Testing#

❌ Only Happy Path Tests#

❌ Output String Matching#

⚠️ Sharp Edges#

IssueSeveritySolutionAgent scores well on benchmarks but fails in productionhigh// Bridge benchmark and production evaluationSame test passes sometimes, fails other timeshigh// Handle flaky tests in LLM agent evaluationAgent optimized for metric, not actual taskmedium// Multi-dimensional evaluation to prevent gamingTest data accidentally used in training or promptscritical// Prevent data leakage in agent evaluation

Related Skills#

Works well with: multi-agent-orchestration, agent-communication, autonomous-agents