Getting Started with AI Agent Evaluation

AI agents are becoming increasingly complex, handling everything from customer support to code generation. But how do you know if your agent is actually performing well? This guide walks you through the fundamentals of AI agent evaluation.

Why Evaluate AI Agents?

Before deploying an AI agent to production, you need confidence that it will:

• Provide accurate responses - minimize hallucinations and errors
• Handle edge cases - gracefully manage unexpected inputs
• Meet performance requirements - respond within acceptable latency
• Maintain safety - avoid harmful or inappropriate outputs

Key Evaluation Metrics

Accuracy Metrics

Metric	Description	When to Use
Factual Accuracy	% of claims that are verifiable	Knowledge-based tasks
Task Completion	% of tasks successfully completed	Agentic workflows
Relevance Score	How relevant responses are to queries	Search/retrieval

Quality Metrics

from future_agi import Evaluator

evaluator = Evaluator()

# Evaluate response quality
result = evaluator.evaluate(
    input=user_query,
    output=agent_response,
    metrics=["coherence", "helpfulness", "safety"]
)

print(f"Quality Score: {result.overall_score}")

Performance Metrics

Track latency at different percentiles:

• p50: Median response time
• p95: 95th percentile (most users experience this or better)
• p99: 99th percentile (worst-case for most requests)

Building Your Evaluation Pipeline

Step 1: Create Test Cases

Start with a diverse set of test cases:

test_cases = [
    {
        "input": "What is our refund policy?",
        "expected_topics": ["refund", "30 days", "conditions"],
        "category": "policy_question"
    },
    {
        "input": "I want to cancel my subscription",
        "expected_action": "trigger_cancellation_flow",
        "category": "action_request"
    }
]

Step 2: Run Evaluations

from future_agi import TestRunner

runner = TestRunner(agent=your_agent)
results = runner.run(test_cases)

# Generate report
results.to_report("evaluation_results.html")

Step 3: Analyze Results

Look for patterns in failures:

• Are certain categories performing worse?
• Do longer inputs cause more errors?
• Are there specific topics that trigger hallucinations?

Continuous Evaluation

Evaluation shouldn’t stop at deployment. Set up continuous monitoring:

from future_agi import Monitor

monitor = Monitor(
    agent=your_agent,
    sample_rate=0.1,  # Evaluate 10% of production traffic
    alerts=[
        {"metric": "accuracy", "threshold": 0.9, "action": "alert"},
        {"metric": "latency_p95", "threshold": 2000, "action": "alert"}
    ]
)

Best Practices

1. Start with clear success criteria - define what “good” looks like
2. Use diverse test data - cover edge cases and different user types
3. Evaluate holistically - accuracy alone isn’t enough
4. Automate where possible - but keep humans in the loop
5. Track trends over time - catch regressions early

Next Steps

Ready to start evaluating your AI agents?

• Sign up for Future AGI - free tier available
• Read our docs - detailed integration guides
• Join our community - get help from experts